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University of Notre Dame
1.
Lei Meng.
Computational Strategies for Analyzing Dynamic and
Heterogeneous Networks and Their Interdisciplinary
Implications</h1>.
Degree: Computer Science and Engineering, 2016, University of Notre Dame
URL: https://curate.nd.edu/show/hx11xd09s3d 
► Networks have been used to model a variety of real-world phenomena in many domains. Due to limitations of techniques for data collection, traditional network…
(more)
▼ Networks have been used to model a
variety of real-world phenomena in many domains. Due to limitations
of techniques for data collection, traditional
network research has
typically focused on studying static and homogenous networks.
However, many interactions (e.g., social communications or
relationships between biomolecules) are evolving and vary in type.
With the recent advancement of data collection techniques,
increasing amounts of dynamic and heterogeneous
network data are
becoming available. Extracting knowledge from such data is a
non-trivial task due to the lack of methods for their analyses and
consequently many challenging questions have emerged both on the
computational as well as the application
side. Therefore, this Ph.D. dissertation focuses
on developing computational strategies for analyzing dynamic and
heterogeneous networks and studying their interdisciplinary
implications. Here, we explore the domains of social and biological
networks, although the strategies are applicable to other domains
as well. In particular, we are interested in three key questions:
1) Will studying data via heterogeneous
network analysis result in
different findings compared to studying the same data via
homogeneous
network analysis? 2) How to systematically analyze
network data that is both heterogeneous and dynamic? 3) How to
efficiently compare two heterogeneous yet related networks via
network alignment? To this end, we: 1) integrate
heterogenous
network data and demonstrate that our approach reveals
additional information that is missed by simpler approaches such as
homogenous
network analysis, by exploring a smartphone study
encompassing multiple link types and node traits; 2) introduce a
novel computational framework for systematic analysis of dynamic
and heterogeneous networks, which we use to link individuals’
evolving social
network positions with their traits, revealing in
the process additional links that are missed by simpler approaches
such as static
network analysis or that have not been studied to
date; and 3) introduce the first ever comparison of two
complementary types of
network alignment methods (local and global)
and propose a new algorithm, IGLOO (Integrating Global and LOcal
biOlogical
network alignment), to reconcile the two, demonstrating
in the process the superiority of IGLOO over each
network alignment
type individually.
Advisors/Committee Members: Nitesh Chawla, Committee Member, David Hachen, Committee Member, Aaron Striegel, Research Director, Tijana Milenkovic, Research Director, Gregory Madey, Committee Member.
Subjects/Keywords: Complex network
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APA (6th Edition):
Meng, L. (2016). Computational Strategies for Analyzing Dynamic and
Heterogeneous Networks and Their Interdisciplinary
Implications</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/hx11xd09s3d
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Meng, Lei. “Computational Strategies for Analyzing Dynamic and
Heterogeneous Networks and Their Interdisciplinary
Implications</h1>.” 2016. Thesis, University of Notre Dame. Accessed April 18, 2021.
https://curate.nd.edu/show/hx11xd09s3d.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Meng, Lei. “Computational Strategies for Analyzing Dynamic and
Heterogeneous Networks and Their Interdisciplinary
Implications</h1>.” 2016. Web. 18 Apr 2021.
Vancouver:
Meng L. Computational Strategies for Analyzing Dynamic and
Heterogeneous Networks and Their Interdisciplinary
Implications</h1>. [Internet] [Thesis]. University of Notre Dame; 2016. [cited 2021 Apr 18].
Available from: https://curate.nd.edu/show/hx11xd09s3d.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Meng L. Computational Strategies for Analyzing Dynamic and
Heterogeneous Networks and Their Interdisciplinary
Implications</h1>. [Thesis]. University of Notre Dame; 2016. Available from: https://curate.nd.edu/show/hx11xd09s3d
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Clemson University
2.
Wu, Bo.
Complex Network Analysis and the Applications in Vehicle Delay-Tolerant Networks.
Degree: PhD, Electrical and Computer Engineering (Holcomb Dept. of), 2016, Clemson University
URL: https://tigerprints.clemson.edu/all_dissertations/2312
► Vehicle Delay Tolerant Networks (VDTNs) is a particular kind of Delay Tolerant Networks (DTNs), where vehicles equipped with transmission capabilities are interconnected to form…
(more)
▼ Vehicle Delay Tolerant Networks (VDTNs) is a particular kind of Delay Tolerant Networks (DTNs), where vehicles equipped with transmission capabilities are interconnected to form Vehicle NETworks (VNETs). Some applications and services on the top of VDTNs have raised a lot of attention, especially by providing information about weather conditions, road safety, traffic jams, speed limit, and even video streamings without the need of infrastructures. However, due to features such as high vehicle mobility, dynamic scenarios, sparsity of vehicles, short contact durations, disruption and intermittent connectivity and strict requirements for latency, many VDTNs do not present satisfactory performance, because no path exists between a source and its target. In this dissertation, we propose three routing methods to solve the problem as follows.
Our first VDTN system focuses on the multi-copy routing in Vehicle Delay Tolerant Networks (VDTNs). Multi-copy routing can balance the
network congestion caused by broadcasting and the efficiency limitation in single-copy routing. However, the different copies of each packet search the destination node independently in current multi-copy routing algorithms, which leads to a low utilization of copies since they may search through the same path repeatedly without cooperation. To solve this problem, we propose a fractal Social community based efficient multi-coPy routing in VDTNs, namely SPread. First, we measure social
network features in Vehicle NETworks (VNETs). Then, by taking advantage of weak ties and fractal structure feature of the community in VNETs, SPread carefully scatters different copies of each packet to different communities that are close to the destination community, thus ensuring that different copies search the destination community through different weak ties. For the routing of each copy, current routing algorithms either fail to exploit reachability information of nodes to different nodes (centrality based methods) or only use single-hop reachability information (community based methods), e.g., similarity and probability. Here, the reachability of node i to a destination j (a community or a node) means the possibility that a packet can reach j through i. In order to overcome above drawbacks, inspired by the personalized PageRank algorithm, we design new algorithms for calculating multi-hop reachability of vehicles to different communities and vehicles dynamically. Therefore, the routing efficiency of each copy can be enhanced. Finally, extensive trace-driven simulation demonstrates the high efficiency of SPread in comparison with state-of-the-art routing algorithms in DTNs.
However, in SPread, we only consider the VNETs as
complex networks and fail to use the unique location information to improve the routing performance. We believe that the
complex network knowledge should be combined with special features of various networks themselves in order to benefit the real application better. Therefore, we further explore the possibility to…
Advisors/Committee Members: Haiying Shen, Adam Hoover, Ilya Safro, Yongqiang Wang.
Subjects/Keywords: complex network; delay tolerant network; vehicle network
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APA (6th Edition):
Wu, B. (2016). Complex Network Analysis and the Applications in Vehicle Delay-Tolerant Networks. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2312
Chicago Manual of Style (16th Edition):
Wu, Bo. “Complex Network Analysis and the Applications in Vehicle Delay-Tolerant Networks.” 2016. Doctoral Dissertation, Clemson University. Accessed April 18, 2021.
https://tigerprints.clemson.edu/all_dissertations/2312.
MLA Handbook (7th Edition):
Wu, Bo. “Complex Network Analysis and the Applications in Vehicle Delay-Tolerant Networks.” 2016. Web. 18 Apr 2021.
Vancouver:
Wu B. Complex Network Analysis and the Applications in Vehicle Delay-Tolerant Networks. [Internet] [Doctoral dissertation]. Clemson University; 2016. [cited 2021 Apr 18].
Available from: https://tigerprints.clemson.edu/all_dissertations/2312.
Council of Science Editors:
Wu B. Complex Network Analysis and the Applications in Vehicle Delay-Tolerant Networks. [Doctoral Dissertation]. Clemson University; 2016. Available from: https://tigerprints.clemson.edu/all_dissertations/2312
Texas A&M University
3.
Lv, Dan.
Modeling Cascading Failures in Complex Networks.
Degree: PhD, Electrical Engineering, 2017, Texas A&M University
URL: http://hdl.handle.net/1969.1/165947
► Large-scale cascading failures can be triggered by very few initial failures, leading to severe damages in complex networks. As modern society becomes more and more…
(more)
▼ Large-scale cascading failures can be triggered by very few initial failures, leading to severe damages in
complex networks. As modern society becomes more and more networked, there is an increasing requirement of security and reliability of
complex networks such as infrastructure networks and cyber networks. In order to design networks which are robust to attacks and enhance the security of the existing networks, this paper studies load-dependent cascading failures in random networks consisting of a large but finite number of components. Under a random single-node attack, a framework is developed to quantify the damage at each stage of a cascade. We mainly use probability theory to analyze the cascade process and use simulations to verify our conclusion. In our result, estimations for the fraction of failed nodes are presented to evaluate the time-dependent system damage due to the attack. Furthermore, the analysis reveals a phase transition behavior in the extent of the damage as the load margin grows. That is, the fraction of the damaged components drops from near one to near zero over a slight change in the load margin. The critical value of the load margin and the short interval over which such an abrupt change occurs are derived to characterize the
network reaction to small
network load variations. Our findings provide design principles for enhancing the
network resiliency and provide guidelines for choosing the load margin to avoid a cascade of failures in load-dependent
complex networks with practical sizes.
Advisors/Committee Members: Liu, Tie (advisor), Cui, Shuguang (committee member), Xie, Le (committee member), Sang, Huiyan (committee member).
Subjects/Keywords: cascading failure; complex network
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APA (6th Edition):
Lv, D. (2017). Modeling Cascading Failures in Complex Networks. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/165947
Chicago Manual of Style (16th Edition):
Lv, Dan. “Modeling Cascading Failures in Complex Networks.” 2017. Doctoral Dissertation, Texas A&M University. Accessed April 18, 2021.
http://hdl.handle.net/1969.1/165947.
MLA Handbook (7th Edition):
Lv, Dan. “Modeling Cascading Failures in Complex Networks.” 2017. Web. 18 Apr 2021.
Vancouver:
Lv D. Modeling Cascading Failures in Complex Networks. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/1969.1/165947.
Council of Science Editors:
Lv D. Modeling Cascading Failures in Complex Networks. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/165947
Delft University of Technology
4.
Wu, J. (author).
Evolving Properties of Growing Networks.
Degree: 2009, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:ac67cd08-0c39-49b9-8967-12186175a644
► Complex networks describe a wide range of systems and structures in the world. Any real network can be modeled as graph, expressed by an adjacency…
(more)
▼ Complex networks describe a wide range of systems and structures in the world. Any real network can be modeled as graph, expressed by an adjacency matrix or list. In many complex networks, when a graph of a certain type grows in size, its properties are expected to change. Each complex network presents specific topological features which characterize its individual properties and are influenced by the dynamics of processes executed on the network. The analysis of complex networks therefore relies on the use of measurements capable of expressing the most relevant topological features. Therefore, understanding and analyzing the properties of different sized graphs is a challenging topic in the research field. The objective of the thesis is to understand the evolving properties of growing networks. Therefore it focuses on comparison of topological metrics with different number of nodes and links. Growing graphs will be approached by two different schemes: preferential link attachment and random link attachment. Several common types of graph models are involved in the thesis. And we also consider different real-world network examples. With the analysis and comparison of numerical simulation results, we want to understand the changing tendency of topological metrics for evolving networks. In final, the thesis reveals different crucial factors affecting the evolving properties of growing network and concludes evolving properties based on both empirical and analytical results.
Telecommunications
Electrical Engineering, Mathematics and Computer Science
Advisors/Committee Members: Hernandez, J.M. (mentor), Van Mieghem, P.F.A. (mentor).
Subjects/Keywords: complex network
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APA (6th Edition):
Wu, J. (. (2009). Evolving Properties of Growing Networks. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:ac67cd08-0c39-49b9-8967-12186175a644
Chicago Manual of Style (16th Edition):
Wu, J (author). “Evolving Properties of Growing Networks.” 2009. Masters Thesis, Delft University of Technology. Accessed April 18, 2021.
http://resolver.tudelft.nl/uuid:ac67cd08-0c39-49b9-8967-12186175a644.
MLA Handbook (7th Edition):
Wu, J (author). “Evolving Properties of Growing Networks.” 2009. Web. 18 Apr 2021.
Vancouver:
Wu J(. Evolving Properties of Growing Networks. [Internet] [Masters thesis]. Delft University of Technology; 2009. [cited 2021 Apr 18].
Available from: http://resolver.tudelft.nl/uuid:ac67cd08-0c39-49b9-8967-12186175a644.
Council of Science Editors:
Wu J(. Evolving Properties of Growing Networks. [Masters Thesis]. Delft University of Technology; 2009. Available from: http://resolver.tudelft.nl/uuid:ac67cd08-0c39-49b9-8967-12186175a644
University of New Mexico
5.
Siddique, Abu Bakar.
Symmetries in the time-averaged dynamics of stochastic models of networks dynamics.
Degree: Mechanical Engineering, 2016, University of New Mexico
URL: http://hdl.handle.net/1928/32987
► In recent years a large body of research has investigated the dynamics of complex networks, including percolation [1, 2], epidemics [3, 4], synchronization [5, 6],…
(more)
▼ In recent years a large body of research has investigated the dynamics of
complex networks, including percolation [1, 2], epidemics [3, 4], synchronization [5, 6], evolutionary game theory [7, 8], and traffic dynamics [9, 10, 11]. These study apply to technological networks, biological networks, and social networks. In general, it has been shown that the topology of these networks (e.g. the degree distribution [12, 13], degree correlation [14, 15], community structure [16], etc.) plays a significant role in their dynamical time evolution.
Advisors/Committee Members: Sorrentino, Francesco, Sorrentino, Francesco, Shen, Yu-Lin, Kouzehgarani, Asal Naseri.
Subjects/Keywords: symmetry; dynamical systems; complex network
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APA (6th Edition):
Siddique, A. B. (2016). Symmetries in the time-averaged dynamics of stochastic models of networks dynamics. (Masters Thesis). University of New Mexico. Retrieved from http://hdl.handle.net/1928/32987
Chicago Manual of Style (16th Edition):
Siddique, Abu Bakar. “Symmetries in the time-averaged dynamics of stochastic models of networks dynamics.” 2016. Masters Thesis, University of New Mexico. Accessed April 18, 2021.
http://hdl.handle.net/1928/32987.
MLA Handbook (7th Edition):
Siddique, Abu Bakar. “Symmetries in the time-averaged dynamics of stochastic models of networks dynamics.” 2016. Web. 18 Apr 2021.
Vancouver:
Siddique AB. Symmetries in the time-averaged dynamics of stochastic models of networks dynamics. [Internet] [Masters thesis]. University of New Mexico; 2016. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/1928/32987.
Council of Science Editors:
Siddique AB. Symmetries in the time-averaged dynamics of stochastic models of networks dynamics. [Masters Thesis]. University of New Mexico; 2016. Available from: http://hdl.handle.net/1928/32987
University of Sydney
6.
Hu, JIngming.
Spectral Sampling for Visual Analytics of Big Complex Networks
.
Degree: 2019, University of Sydney
URL: http://hdl.handle.net/2123/21263
► Visual analysis is one of the most effective methods of analyzing large complex networks, and diverse research directions for analyzing and sampling large complex networks…
(more)
▼ Visual analysis is one of the most effective methods of analyzing large complex networks, and diverse research directions for analyzing and sampling large complex networks are being pursued. One approach is to replace the original graph with a much smaller one while maintaining high quality; this is called the proxy graph approach. However, research has demonstrated that it is a challenge to compute a high-quality proxy graph to represent the original graph. It is also expensive to label the structural properties of the network, especially in terms of time consumption. This thesis introduces new methods for computing proxy graphs based on spectral sparsification approaches for visualizing large complex networks. Two types of spectral sparsification approaches are proposed: 1. We introduce a new method called spectral sampling vertex (SV) for computing proxy graphs. This method reduces the number of vertices in a graph while retaining its structural properties, based on the high effective resistance value. Extensive experimental results using graph sampling quality metrics, visual comparison, and proxy quality metrics confirm that our new method significantly outperforms the Random Vertex sampling method and the Degree Centrality-based sampling method. 2. We introduced two divide and conquer methods for spectral sparsification: BC Tree-based Spectral Sparisification (BC_SS) and BC Tree-based Spectral Vertex Sampling (BC_SV). These two methods are based on the decomposition of a connected graph into biconnected components. Experimental results show that our methods are significantly faster than the pre- vious method while preserving similar sparsification results.
Subjects/Keywords: Complex network;
Visual Analytics
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APA ·
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Manager
APA (6th Edition):
Hu, J. (2019). Spectral Sampling for Visual Analytics of Big Complex Networks
. (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/21263
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Hu, JIngming. “Spectral Sampling for Visual Analytics of Big Complex Networks
.” 2019. Thesis, University of Sydney. Accessed April 18, 2021.
http://hdl.handle.net/2123/21263.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Hu, JIngming. “Spectral Sampling for Visual Analytics of Big Complex Networks
.” 2019. Web. 18 Apr 2021.
Vancouver:
Hu J. Spectral Sampling for Visual Analytics of Big Complex Networks
. [Internet] [Thesis]. University of Sydney; 2019. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/2123/21263.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Hu J. Spectral Sampling for Visual Analytics of Big Complex Networks
. [Thesis]. University of Sydney; 2019. Available from: http://hdl.handle.net/2123/21263
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Notre Dame
7.
Yuriy Hulovatyy.
Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>.
Degree: Computer Science and Engineering, 2016, University of Notre Dame
URL: https://curate.nd.edu/show/1r66j100v5f
► Networks provide a natural and powerful way to model complex real-world systems in various domains. Studying structure of a network can help extract functional…
(more)
▼ Networks provide a natural and powerful way
to model
complex real-world systems in various domains. Studying
structure of a
network can help extract functional knowledge about
the corresponding system. As real-world networks exhibit
non-trivial organization at many scales, this extraction can be
done on different levels: from the global perspective of the whole
network to the intermediate perspective of node groups (or
communities) to the local perspective of individual nodes. With new
technological advances, the amount of available real-world
network
data in different domains rapidly increases. In addition, networks
are growing in size and complexity. For example, whereas
traditional
network data has been static, because it has become
easier to record system evolution, more of dynamic
network data is
becoming available. For these reasons, it is critical to develop
novel computational strategies for efficient extraction of
functional information from the structure of such
complex (e.g.,
dynamic) networks. And this is the main focus of this dissertation.
We achieve this goal in two different ways, by: 1) answering novel
research questions via established
network approaches, and 2)
developing novel
network approaches for established research
questions. In the first context, we apply global
network analysis to answer a novel question in a novel domain in
which
network research has not been used to date – interpreting
affective physiological data. In addition, we employ local
network
analysis to study the interplay between individuals’ social
interactions and traits from a new dynamic (rather than traditional
static)
network viewpoint. In the second context,
we take a well-established local analysis approach for static
networks to develop a novel method for the problem of link
prediction, which we use for de-noising biological networks.
Moreover, we take the same static local approach and develop new
theory for dynamic
network analysis. We demonstrate that accounting
for temporal information helps and use our method to study human
aging from biological networks. Finally, we introduce a new
approach for studying dynamic networks from the intermediate
perspective, which deals with the problem of segment community
detection. We show that our approach outperforms existing methods
in terms of both accuracy and computational
complexity.
Advisors/Committee Members: Sidney D'Mello, Committee Member, Nitesh Chawla, Committee Member, Aaron Striegel, Committee Member, Tijana Milenkovic, Research Director.
Subjects/Keywords: Complex networks; Network evolution; Network analysis
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APA ·
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Manager
APA (6th Edition):
Hulovatyy, Y. (2016). Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/1r66j100v5f
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Hulovatyy, Yuriy. “Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>.” 2016. Thesis, University of Notre Dame. Accessed April 18, 2021.
https://curate.nd.edu/show/1r66j100v5f.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Hulovatyy, Yuriy. “Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>.” 2016. Web. 18 Apr 2021.
Vancouver:
Hulovatyy Y. Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>. [Internet] [Thesis]. University of Notre Dame; 2016. [cited 2021 Apr 18].
Available from: https://curate.nd.edu/show/1r66j100v5f.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Hulovatyy Y. Exploring Structure and Dynamics of Complex Networks: Novel
Methods and Interdisciplinary Applications</h1>. [Thesis]. University of Notre Dame; 2016. Available from: https://curate.nd.edu/show/1r66j100v5f
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Virginia Tech
8.
Khorramzadeh, Yasamin.
Network Reliability: Theory, Estimation, and Applications.
Degree: PhD, Physics, 2015, Virginia Tech
URL: http://hdl.handle.net/10919/64383
► Network reliability is the probabilistic measure that determines whether a network remains functional when its elements fail at random. Definition of functionality varies depending on…
(more)
▼ Network reliability is the probabilistic measure that determines whether a
network remains functional when its elements fail at random. Definition of functionality varies depending on the problem of interest, thus
network reliability has much potential as a unifying framework to study a broad range of problems arising in
complex network contexts. However, since its introduction in the 1950's,
network reliability has remained more of an interesting theoretical construct than a practical tool. In large part, this is due to well-established complexity costs for both its evaluation and approximation, which has led to the classification of
network reliability as a NP-Hard problem. In this dissertation we present an algorithm to estimate
network reliability and then utilize it to evaluate the reliability of large networks under various descriptions of functionality.
The primary goal of this dissertation is to pose
network reliability as a general scheme that provides a practical and efficiently computable observable to distinguish different networks. Employing this concept, we are able to demonstrate how local structural changes can impose global consequences. We further use
network reliability to assess the most critical
network entities which ensure a
network's reliability. We investigate each of these aspects of reliability by demonstrating some example applications.
Advisors/Committee Members: Eubank, Stephen G. (committeechair), Tauber, Uwe C. (committeechair), Scarola, Vito W. (committee member), Pleimling, Michel Jean (committee member), Heremans, Jean Joseph (committee member).
Subjects/Keywords: Complex Networks; Network Reliability; Network Topology
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APA (6th Edition):
Khorramzadeh, Y. (2015). Network Reliability: Theory, Estimation, and Applications. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64383
Chicago Manual of Style (16th Edition):
Khorramzadeh, Yasamin. “Network Reliability: Theory, Estimation, and Applications.” 2015. Doctoral Dissertation, Virginia Tech. Accessed April 18, 2021.
http://hdl.handle.net/10919/64383.
MLA Handbook (7th Edition):
Khorramzadeh, Yasamin. “Network Reliability: Theory, Estimation, and Applications.” 2015. Web. 18 Apr 2021.
Vancouver:
Khorramzadeh Y. Network Reliability: Theory, Estimation, and Applications. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/10919/64383.
Council of Science Editors:
Khorramzadeh Y. Network Reliability: Theory, Estimation, and Applications. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/64383
Northeastern University
9.
Jin, Qing.
Statistical physics of complex substitutive systems.
Degree: PhD, Department of Physics, 2017, Northeastern University
URL: http://hdl.handle.net/2047/D20260325
► Diffusion processes are central to human interactions. Despite extensive studies that span multiple disciplines, our knowledge is limited to spreading processes in non-substitutive systems. Yet,…
(more)
▼ Diffusion processes are central to human interactions. Despite extensive studies that span multiple disciplines, our knowledge is limited to spreading processes in non-substitutive systems. Yet, a considerable number of ideas, products, and behaviors spread by substitution; to adopt a new one, agents must give up an existing one. This captures the spread of scientific constructs – forcing scientists to choose, for example, a deterministic or probabilistic worldview, as well as the adoption of durable items, such as mobile phones, cars, or homes. In this dissertation, I develop a statistical physics framework to describe, quantify, and understand substitutive systems. By empirically exploring three collected high-resolution datasets pertaining to such systems, I build a mechanistic model describing substitutions, which not only analytically predicts the universal macroscopic phenomenon discovered in the collected datasets, but also accurately captures the trajectories of individual items in a complex substitutive system, demonstrating a high degree of regularity and universality in substitutive systems. I also discuss the origins and insights of the parameters in the substitution model and possible generalization form of the mathematical framework. The systematical study of substitutive systems presented in this dissertation could potentially guide the understanding and prediction of all spreading phenomena driven by substitutions, from electric cars to scientific paradigms, and from renewable energy to new healthy habits.
Subjects/Keywords: complex network; complex substitutive system; complex system; statistical physics
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APA (6th Edition):
Jin, Q. (2017). Statistical physics of complex substitutive systems. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20260325
Chicago Manual of Style (16th Edition):
Jin, Qing. “Statistical physics of complex substitutive systems.” 2017. Doctoral Dissertation, Northeastern University. Accessed April 18, 2021.
http://hdl.handle.net/2047/D20260325.
MLA Handbook (7th Edition):
Jin, Qing. “Statistical physics of complex substitutive systems.” 2017. Web. 18 Apr 2021.
Vancouver:
Jin Q. Statistical physics of complex substitutive systems. [Internet] [Doctoral dissertation]. Northeastern University; 2017. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/2047/D20260325.
Council of Science Editors:
Jin Q. Statistical physics of complex substitutive systems. [Doctoral Dissertation]. Northeastern University; 2017. Available from: http://hdl.handle.net/2047/D20260325
University of New South Wales
10.
Hossain, Md Murad.
On Capacity Estimation and Capacity-Safety Relationship in an Air Transportation Network.
Degree: Engineering & Information Technology, 2016, University of New South Wales
URL: http://handle.unsw.edu.au/1959.4/56624
;
https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41059/SOURCE01?view=true
► Air transportation is a complex system of interlinked distributed networks in which different components have their own constraints and performance measures. For example, an airport…
(more)
▼ Air transportation is a
complex system of interlinked distributed networks in which different components have their own constraints and performance measures. For example, an airport
network in which each airport is treated as a node and models the departures/arrivals of flights as links considers capacity as its limiting factor. Whereas, an airspace
network that consists of airways (as links) and waypoints (as nodes) providing an orderly flow of air traffic and safe separation between flights considers collision risk as its limiting factor. To accommodate the increasing demand to safely manage air traffic flow, it is imperative to understand the interactions between these two components and the limiting factors that define their characteristics. Understanding this relationship is a major consideration when determining whether and which components should aim to increase safety and capacity. In this thesis, I propose a model for airport
network capacity estimation and a model of airspace
network risk analysis. I then develop a framework for modelling and integrating airport and airspace networks in an overall air transportation system. Finally, I propose a methodology for determining their
complex interactions to analyse the relationship between capacity and safety.One challenge in analysing the capacity-safety relationship for air transportation is measuring its capacity. In air transportation, capacities have traditionally been measured based on the individual elements of the
network, such as links (sector capacity and airspace complexity) and nodes (terminals and runway throughput). These measures obviously do not constitute the overall system-level capacity of a
network. This research involves developing a
network-level capacity estimation model and method. The proposed model does not require knowledge of an individual airport's capacity and offers an understanding of the relationship between the flow capacity and safety metric of its corresponding airspace.Experimental and empirical results establish the nature of the relationship between airport
network capacity and airspace safety when considered in an interacting air transport system. As the hourly flow increases in the airport
network, the overall collision risk increases linearly and, after a certain level, crosses the target level of safety. Such a capacity-safety relationship indicates that the capability of existing air traffic control systems to safely handle projected growth in aircraft operations appears to be artificially limited by the airspace.
Advisors/Committee Members: Alam, Sameer, Engineering & Information Technology, UNSW Canberra, UNSW, Hussein, Abbass, Engineering & Information Technology, UNSW Canberra, UNSW.
Subjects/Keywords: Airspace Network; Air Transportation Network; Airport Network; Network Capacity; Collision Risk; Complex Network
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APA (6th Edition):
Hossain, M. M. (2016). On Capacity Estimation and Capacity-Safety Relationship in an Air Transportation Network. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/56624 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41059/SOURCE01?view=true
Chicago Manual of Style (16th Edition):
Hossain, Md Murad. “On Capacity Estimation and Capacity-Safety Relationship in an Air Transportation Network.” 2016. Doctoral Dissertation, University of New South Wales. Accessed April 18, 2021.
http://handle.unsw.edu.au/1959.4/56624 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41059/SOURCE01?view=true.
MLA Handbook (7th Edition):
Hossain, Md Murad. “On Capacity Estimation and Capacity-Safety Relationship in an Air Transportation Network.” 2016. Web. 18 Apr 2021.
Vancouver:
Hossain MM. On Capacity Estimation and Capacity-Safety Relationship in an Air Transportation Network. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2021 Apr 18].
Available from: http://handle.unsw.edu.au/1959.4/56624 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41059/SOURCE01?view=true.
Council of Science Editors:
Hossain MM. On Capacity Estimation and Capacity-Safety Relationship in an Air Transportation Network. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/56624 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:41059/SOURCE01?view=true
Penn State University
11.
Gomez Tejeda Zanudo, Jorge.
Network-based dynamic modeling and control strategies in complex diseases.
Degree: 2016, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/27687
► In order to understand how the interactions of molecular components inside cells give rise to cellular function, creating models that incorporate the current biological knowledge…
(more)
▼ In order to understand how the interactions of molecular components inside cells give rise to cellular function, creating models that incorporate the current biological knowledge while also making testable predictions that guide experimental work is of utmost importance. Creating such models is a challenging task in
complex diseases such as cancer, in which numerous components are known to play an important role. To model the dynamics of the networks underlying
complex diseases I use
network-based models with discrete dynamics, which have been shown to reproduce the qualitative dynamics of a multitude of cellular systems while requiring only the combinatorial nature of the interactions and qualitative information on the desired/undesired states.
I developed analytical and computational tools based on a type of function-dependent subnetwork that stabilizes in a steady state regardless of the state of the rest of the
network, and which I termed stable motif. Based on the concept of stable motif, I proposed a method to identify a model's dynamical attractors, which have been found to be identifiable with the cell fates and cell behaviors of modeled organisms. I also proposed a stable-motif-based control method that identifies targets whose manipulation ensures the convergence of the model towards an attractor of interest. The identified control targets can be single or multiple nodes, are proven to always drive any initial condition to the desired attractor, and need to be applied only transiently to be effective.
I illustrated the potential of these methods by collaborating with wet-lab cancer biologists to construct and analyze a model for a process involved in the spread of cancer cells (epithelial-mesenchymal transition), and also applied them to several published models for
complex diseases, such as a type of white blood cell cancer (T-LGL leukemia). These methods allowed me to find attractors of larger models than what was previously possible, identify the subnetworks responsible for the disease and the healthy cell states, and show that stabilizing the activity of a few select components can drive the cell towards a desired fate or away from an undesired fate, the validity of which is supported by experimental work.
Advisors/Committee Members: Reka Z Albert, Dissertation Advisor/Co-Advisor, Reka Z Albert, Committee Chair/Co-Chair, Dezhe Jin, Committee Member, Lu Bai, Committee Member, Timothy Reluga, Committee Member, Richard Wallace Robinett, Special Member.
Subjects/Keywords: Systems Biology; Complex Networks; Network models; Cancer
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Export
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APA (6th Edition):
Gomez Tejeda Zanudo, J. (2016). Network-based dynamic modeling and control strategies in complex diseases. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/27687
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Gomez Tejeda Zanudo, Jorge. “Network-based dynamic modeling and control strategies in complex diseases.” 2016. Thesis, Penn State University. Accessed April 18, 2021.
https://submit-etda.libraries.psu.edu/catalog/27687.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Gomez Tejeda Zanudo, Jorge. “Network-based dynamic modeling and control strategies in complex diseases.” 2016. Web. 18 Apr 2021.
Vancouver:
Gomez Tejeda Zanudo J. Network-based dynamic modeling and control strategies in complex diseases. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Apr 18].
Available from: https://submit-etda.libraries.psu.edu/catalog/27687.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Gomez Tejeda Zanudo J. Network-based dynamic modeling and control strategies in complex diseases. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/27687
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Indiana University
12.
Gao, Zheng.
COMMUNITY DETECTION IN GRAPHS
.
Degree: 2020, Indiana University
URL: http://hdl.handle.net/2022/25623
► Community detection has always been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach, community detection aims…
(more)
▼ Community detection has always been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach, community detection aims to explore node high-order closeness by leveraging graph topological structure. By grouping similar nodes or edges into the same community while separating dissimilar ones apart into different communities, graph structure can be revealed in a coarser resolution. It can be beneficial for numerous applications such as user shopping recommendation and advertisement in e-commerce, protein-protein interaction prediction in the bioinformatics, and literature recommendation or scholar collaboration in citation
analysis. However, identifying communities is an ill-defined problem. Due to the No Free Lunch theorem [1], there is neither gold standard to represent perfect community partition nor universal methods that are able to detect satisfied communities for all tasks under various types of graphs. To have a global view of this research topic, I summarize state-of-art community detection methods by categorizing them based on graph types, research tasks and methodology frameworks. As academic exploration on community detection grows rapidly in recent years, I hereby particularly focus on the state-of-art works published in the latest decade, which may leave out some classic models published decades ago. Meanwhile, three subtle community detection tasks are proposed and assessed in this dissertation as well. First, apart from general models which consider only graph structures, personalized community detection considers user need as auxiliary information to guide community detection. In the end, there will be fine-grained communities for nodes better matching user needs while coarser-resolution communities for the rest of less relevant nodes. Second, graphs always suffer from the sparse connectivity issue. Leveraging conventional models directly on such graphs may hugely distort the quality of generate communities. To tackle such a problem, cross-graph techniques are involved to propagate external graph information as a support for target graph community detection. Third, graph community structure supports a natural language processing (NLP) task to depict node intrinsic characteristics by generating node summarizations via a text generative model. The contribution of this dissertation is threefold. First, a decent amount of researches are reviewed and summarized under a well-defined taxonomy. Existing works about methods, evaluation and applications are all addressed in the literature review. Second, three novel community detection tasks are demonstrated and associated models are proposed and evaluated by comparing with state-of-art baselines under various datasets. Third, the limitations of current works are pointed out and future research tracks with potentials are discussed as well.
Advisors/Committee Members: Liu, Xiaozhong (advisor).
Subjects/Keywords: community detection;
complex network analysis;
graph mining
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Manager
APA (6th Edition):
Gao, Z. (2020). COMMUNITY DETECTION IN GRAPHS
. (Thesis). Indiana University. Retrieved from http://hdl.handle.net/2022/25623
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Gao, Zheng. “COMMUNITY DETECTION IN GRAPHS
.” 2020. Thesis, Indiana University. Accessed April 18, 2021.
http://hdl.handle.net/2022/25623.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Gao, Zheng. “COMMUNITY DETECTION IN GRAPHS
.” 2020. Web. 18 Apr 2021.
Vancouver:
Gao Z. COMMUNITY DETECTION IN GRAPHS
. [Internet] [Thesis]. Indiana University; 2020. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/2022/25623.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Gao Z. COMMUNITY DETECTION IN GRAPHS
. [Thesis]. Indiana University; 2020. Available from: http://hdl.handle.net/2022/25623
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Adelaide
13.
Le, Ba Dung.
Community detection in complex networks.
Degree: 2018, University of Adelaide
URL: http://hdl.handle.net/2440/117956
► Complex networks such as social networks and biological networks represent complex systems in the real world. These networks usually consist of communities which are groups…
(more)
▼ Complex networks such as social networks and biological networks represent
complex systems in the real world. These networks usually consist of communities which are groups of nodes with dense connections among nodes in the same group and sparse connections between nodes in different groups. Identifying communities in
complex networks is useful for many real-world applications. Numerous community detection approaches have been investigated over the past decades. Modularity is a well-known function to measure the quality of a
network division into communities. The most popular detection approach is modularity optimization that identifes communities by finding the community division with highest modularity over all possible community divisions of the
network. Current state-of-the-art algorithms for maximizing modularity perform well on networks of strong communities, which have more intra-community connections than inter-community connections. However, these algorithms tend to get trapped in a poor local maximum on networks with weak communities, which have more inter-community connections than intra-community connections. In the first part of this thesis, we develop a new algorithm for maximizing modularity in networks with weak communities. Our proposed algorithm extends the state-of-the-art algorithm LPAm+ by introducing a method to escape local maximum. Our algorithm follows a guided search strategy inspired by the record-to- record travel algorithm for a trade-off between performance and complexity. Experimental results show that our proposed algorithm, named meta-LPAm+, outperforms state-of-the-art algorithms, in terms of modularity, on networks with weak communities while retaining a comparable performance on networks of strong communities. In the second part of this thesis, we study the problem of evaluating community detection algorithms. Evaluating the detection algorithms on networks with known communities is important to estimate the accuracy of the algorithms and to compare different algorithms. Since there are currently only a small number of real networks with known communities available, the detection algorithms are most dominantly tested on synthetic networks with built-in community structure. Current benchmarks, that generate networks with built-in community structure, assign the same fraction of inter-community connections, referred to as the mixing fraction, for every community in the same
network and ignore the presence of noise, or outliers. These existing benchmarks, therefore, cannot capture properties of nodes and communities in real networks. We address this issue by proposing a new benchmark that accounts for the heterogeneity in community mixing fractions and the presence of outliers. Our proposed benchmark extends the state-of-the-art benchmark LFR by incorporating heterogeneous community mixing fractions and outliers. We use our new benchmark to evaluate the performances of existing community detection algorithms. The results show that the variation in community mixing fractions and…
Advisors/Committee Members: Shen, Hong (advisor), Falkner, Nickolas (advisor), Nguyen, Hung (advisor), School of Computer Science (school).
Subjects/Keywords: Community detection; complex networks; network clustering
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Export
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APA (6th Edition):
Le, B. D. (2018). Community detection in complex networks. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/117956
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Le, Ba Dung. “Community detection in complex networks.” 2018. Thesis, University of Adelaide. Accessed April 18, 2021.
http://hdl.handle.net/2440/117956.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Le, Ba Dung. “Community detection in complex networks.” 2018. Web. 18 Apr 2021.
Vancouver:
Le BD. Community detection in complex networks. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/2440/117956.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Le BD. Community detection in complex networks. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/117956
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
14.
Huang, H. (author).
Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm.
Degree: MSComputer Science, 2015, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d
► Complex networks are a special type of graph that frequently appears in nature and in many different fields of science and engineering. Studying complex networks…
(more)
▼ Complex networks are a special type of graph that frequently appears in nature and in many different fields of science and engineering. Studying complex networks is the key to solve the problems in these fields. Complex networks have unique features which we cannot find in regular graphs, and the study of complex networks gives rise to many interesting research questions. An interesting feature to study in complex networks is community structure. Intuitively speaking, communities are group of vertices in a graph that are densely connected with each other in the same group, while sparsely connected with other nodes in the graph. The notion of community has practical significance. Many different concept and phenomenons in real world problems can be translated into communities in a graph, such as politicians with similar opinions in the political opinion network. In this thesis work, a distributed version of a popular community detection method-Louvain method-is developed using graph computation framework Apache Spark GraphX. Characteristics of this algorithm, such as convergence and quality of communities produced, are studied by both theoretical reasoning and experimental evaluation. The result shows that this algorithm can parallelize community detection effectively. This thesis also explores the possibility of using graph sampling to accelerate resolution parameter selection of a resolution-limit-free community detection method. Two sampling algorithms, random node selection and forest fire sampling algorithm, are compared. This comparison leads to suggestions of choice of sampling algorithm and parameter value of the chosen sampling algorithm.
Master of Science Computer Science
Software and Computer Technology
Electrical Engineering, Mathematics and Computer Science
Advisors/Committee Members: Hidders, A.J.H. (mentor), Krings, G. (mentor).
Subjects/Keywords: complex network; community detection; distributed computing
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Manager
APA (6th Edition):
Huang, H. (. (2015). Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d
Chicago Manual of Style (16th Edition):
Huang, H (author). “Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm.” 2015. Masters Thesis, Delft University of Technology. Accessed April 18, 2021.
http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d.
MLA Handbook (7th Edition):
Huang, H (author). “Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm.” 2015. Web. 18 Apr 2021.
Vancouver:
Huang H(. Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Apr 18].
Available from: http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d.
Council of Science Editors:
Huang H(. Design, Analysis and Experimental Evaluation of a Distributed Community Detection Algorithm. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:5ef1696a-0ef8-4d4c-a807-3d0fd3247b1d
15.
Igo Ramalho Brilhante.
Mobility Data under Analysis a Complex Network Perspective from Interactions Among Trajectories to Movements among Points Interest.
Degree: 2012, Universidade Federal do CearÃ; Programa de PÃs-GraduaÃÃo em CiÃncia da ComputaÃÃo; UFC; BR
URL: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7837
► The explosion of personal positioning devices like GPS-enabled smartphones has enabled the collection and storage of a huge amount of positioning data in the form…
(more)
▼ The explosion of personal positioning devices like GPS-enabled smartphones has enabled the collection and storage of a huge amount of positioning data in the form of trajectories. Thereby, trajectory data have brought many research challenges in the process of recovery, storage and knowledge discovery in mobility as well as new applications to support our society in mobility terms. Other research area that has been receiving great attention nowadays is the area of
complex network or science of networks.
Complex network is the first approach to model
complex system that are present in the real world, such as economic markets, the Internet, World Wide Web and disease spreading to name a few. It has been applied in different field, like Computer Science, Biology and Physics. Therefore,
complex networks have demonstrated a great potential to investigate the behavior of
complex systems through their entities and the relationships that exist among them.
The present dissertation, therefore, aims at exploiting approaches to analyze mobility data using a perspective of
complex networks. The first exploited approach stands for the trajectories as the main entities of the networks connecting each other through a similarity function. The second, in turn, focuses on points of interest that are visited by people, which perform some activities in these points. In addition, this dissertation also exploits the proposed methodologies in order to develop a software tool to support users in mobility analysis using
complex network techniques.
Advisors/Committee Members: Josà AntÃnio Fernandes de Macedo, VÃnia Maria Ponte Vidal, Marco AntÃnio Casanova, Chiara Renso.
Subjects/Keywords: Complex Network; Mobility; Trajectory; CIENCIA DA COMPUTACAO
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APA (6th Edition):
Brilhante, I. R. (2012). Mobility Data under Analysis a Complex Network Perspective from Interactions Among Trajectories to Movements among Points Interest. (Masters Thesis). Universidade Federal do CearÃ; Programa de PÃs-GraduaÃÃo em CiÃncia da ComputaÃÃo; UFC; BR. Retrieved from http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7837
Chicago Manual of Style (16th Edition):
Brilhante, Igo Ramalho. “Mobility Data under Analysis a Complex Network Perspective from Interactions Among Trajectories to Movements among Points Interest.” 2012. Masters Thesis, Universidade Federal do CearÃ; Programa de PÃs-GraduaÃÃo em CiÃncia da ComputaÃÃo; UFC; BR. Accessed April 18, 2021.
http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7837.
MLA Handbook (7th Edition):
Brilhante, Igo Ramalho. “Mobility Data under Analysis a Complex Network Perspective from Interactions Among Trajectories to Movements among Points Interest.” 2012. Web. 18 Apr 2021.
Vancouver:
Brilhante IR. Mobility Data under Analysis a Complex Network Perspective from Interactions Among Trajectories to Movements among Points Interest. [Internet] [Masters thesis]. Universidade Federal do CearÃ; Programa de PÃs-GraduaÃÃo em CiÃncia da ComputaÃÃo; UFC; BR; 2012. [cited 2021 Apr 18].
Available from: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7837.
Council of Science Editors:
Brilhante IR. Mobility Data under Analysis a Complex Network Perspective from Interactions Among Trajectories to Movements among Points Interest. [Masters Thesis]. Universidade Federal do CearÃ; Programa de PÃs-GraduaÃÃo em CiÃncia da ComputaÃÃo; UFC; BR; 2012. Available from: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7837
Queens University
16.
Wang, Lili.
Integrative Network Analysis for Understanding Human Complex Traits
.
Degree: Computing, 2015, Queens University
URL: http://hdl.handle.net/1974/13034
► Over the last decade, high throughput biological data, have been accumulating at rapidly increasing rates, providing the opportunity to gain insight into various fundamental biological…
(more)
▼ Over the last decade, high throughput biological data, have been accumulating at rapidly increasing rates, providing the opportunity to gain insight into various fundamental biological processes. Such large-scale data have been explored using network representation and graph theory to study biological relationships. Meanwhile, a great amount of effort has also been dedicated to integrate diverse biological data types in order to build networks and apply computational analysis to distill meaningful information for specific biological problems. As a result, network-based analysis has become a powerful paradigm to model and study large-scale biological data. The goal of network-based analysis of human complex traits is to annotate or predict new relationships between biological entities, such as proteins, drugs and phenotypes. Furthermore, such analysis can facilitate the diagnosis and prognosis of common complex diseases.
This thesis comprises three contributions. First, heterogeneous biological data are integrated and a novel tool has been developed to easily construct and navigate networks representing the large scale data. In addition the resulting networks can be analyzed using computational methods to solve specific biological problems. Second, an integrative network-based pathway analysis for genome-wide association studies (GWAS) has been proposed to take advantage of the large scale network to combine topological connectivity with signals from GWAS in order to detect enriched pathways. Third, an integrative strategy combines multiple quantitative profiles with a large scale network to assist the biomarker selection for ovarian cancer using two different computational methods: (A) an aggregate ranking to score the candidate proteins and (B) pathway analysis to find enriched sub-networks.
These three contributions demonstrate a pipeline to model large heterogeneous biological data in terms of networks and conduct network-based analysis for understanding the molecular basis of human diseases.
Subjects/Keywords: Human Complex Traits
;
Complex Diseases
;
Biological Network
;
Biological Data
;
Integrative
;
Network Analysis
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Manager
APA (6th Edition):
Wang, L. (2015). Integrative Network Analysis for Understanding Human Complex Traits
. (Thesis). Queens University. Retrieved from http://hdl.handle.net/1974/13034
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Wang, Lili. “Integrative Network Analysis for Understanding Human Complex Traits
.” 2015. Thesis, Queens University. Accessed April 18, 2021.
http://hdl.handle.net/1974/13034.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wang, Lili. “Integrative Network Analysis for Understanding Human Complex Traits
.” 2015. Web. 18 Apr 2021.
Vancouver:
Wang L. Integrative Network Analysis for Understanding Human Complex Traits
. [Internet] [Thesis]. Queens University; 2015. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/1974/13034.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wang L. Integrative Network Analysis for Understanding Human Complex Traits
. [Thesis]. Queens University; 2015. Available from: http://hdl.handle.net/1974/13034
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
17.
Lu, Zhixin.
DYNAMICS OF LARGE SYSTEMS OF NONLINEARLY EVOLVING UNITS.
Degree: Chemical Physics, 2017, University of Maryland
URL: http://hdl.handle.net/1903/19944
► The dynamics of large systems of many nonlinearly evolving units is a general research area that has great importance for many areas in science and…
(more)
▼ The dynamics of large systems of many nonlinearly evolving units is a general research area that has great importance for many areas in science and technology, including biology, computation by artificial neural networks, statistical mechanics, flocking in animal groups, the dynamics of coupled neurons in the brain, and many others. While universal principles and techniques are largely lacking in this broad
area of research, there is still one particular phenomenon that seems to be broadly applicable. In particular, this is the idea of emergence, by which is meant macroscopic behaviors that “emerge” from a large system of many “smaller or simpler entities such that ... large entities” [i.e., macroscopic behaviors] arise which “exhibit properties the smaller/simpler entities do not exhibit.” [1]. In this thesis we investigate mechanisms and manifestations of emergence in four dynamical systems consisting many nonlinearly evolving units. These four systems are as follows.
(a) We first study the motion of a large ensemble of many noninteracting particles in a slowly changing Hamiltonian system that undergoes a separatrix crossing. In such systems, we find that separatrix-crossing induces a counterintuitive effect. Specifically, numerical simulation of two sets of densely sprinkled initial conditions on two energy curves appears to suggest that the two energy curves, one originally enclosing the other, seemingly interchange their positions. This, however, is topologically forbidden. We resolve this paradox by introducing a numerical simulation method we call “robust” and study its consequences.
(b) We next study the collective dynamics of oscillatory pacemaker neurons in Suprachiasmatic Nucleus (SCN), which, through synchrony, govern the circadian rhythm of mammals. We start from a high-dimensional description of the many coupled oscillatory neuronal units within the SCN. This description is based on a forced Kuramoto model. We then reduce the system dimensionality by using the Ott Antonsen Ansatz and obtain a low-dimensional macroscopic description. Using this reduced macroscopic system, we explain the east-west asymmetry of jet-lag recovery and discus the consequences of our findings.
(c) Thirdly, we study neuron firing in integrate-and-fire neural networks. We build a discrete-state/discrete-time model with both excitatory and inhibitory neurons and find a phase transition between avalanching dynamics and ceaseless firing dynamics. Power-law firing avalanche size/duration distributions are observed at critical parameter values. Furthermore, in this critical regime we find the same power law exponents as those observed from experiments and previous, more restricted, simulation studies. We also employ a mean-field method and show that inhibitory neurons in this system promote robustness of the criticality (i.e., an enhanced range of system parameter where power-law avalanche statistics applies).
(d) Lastly, we study the dynamics of “reservoir computing networks” (RCN’s), which is a recurrent neural
network (RNN)…
Advisors/Committee Members: Ott, Edward (advisor).
Subjects/Keywords: Physics; Applied mathematics; Chaos; Complex Network; Complex System; Machine Learning; Neural Network; Nonlinear Dynamics
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APA (6th Edition):
Lu, Z. (2017). DYNAMICS OF LARGE SYSTEMS OF NONLINEARLY EVOLVING UNITS. (Thesis). University of Maryland. Retrieved from http://hdl.handle.net/1903/19944
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Lu, Zhixin. “DYNAMICS OF LARGE SYSTEMS OF NONLINEARLY EVOLVING UNITS.” 2017. Thesis, University of Maryland. Accessed April 18, 2021.
http://hdl.handle.net/1903/19944.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lu, Zhixin. “DYNAMICS OF LARGE SYSTEMS OF NONLINEARLY EVOLVING UNITS.” 2017. Web. 18 Apr 2021.
Vancouver:
Lu Z. DYNAMICS OF LARGE SYSTEMS OF NONLINEARLY EVOLVING UNITS. [Internet] [Thesis]. University of Maryland; 2017. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/1903/19944.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lu Z. DYNAMICS OF LARGE SYSTEMS OF NONLINEARLY EVOLVING UNITS. [Thesis]. University of Maryland; 2017. Available from: http://hdl.handle.net/1903/19944
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Melbourne
18.
Songhori, Mohsen Jafari.
Modeling of complex product development and complex supply networks.
Degree: 2013, University of Melbourne
URL: http://hdl.handle.net/11343/39790
► Complex products have been of interest to academics and practitioners. This interest has recently been intensifying as the set of complex products are growing by…
(more)
▼ Complex products have been of interest to academics and practitioners. This interest has recently been intensifying as the set of complex products are growing by advancements in science and technology. This thesis studies these products from the product development and supply chain perspectives. Utilizing these perspectives, complex products are complex systems. As complex systems, they have hierarchy orders in which some elements have more influence on others. Deploying the Product Development (PD) perspective, product and organizational architec- tures of complex products have been expected to be associated. However, the empirical observa- tions have partially supported this expectation. Therefore, misalignments can occur in complex product development. Despite their occurrence, the literature of complex products lacks models that examine the PD performance effects of misalignments.
Using the Complex Adaptive Systems (CAS) view, the PD part of this research develops a NK(C) fitness landscape simulation model. In the developed model, PD teams search on a per- ceived landscape rather than the real landscape that makes them undergo some performance degra- dations. Such performance degradations occur as two types of errors: Type I errors of rejecting a superior design and Type II errors of accepting an inferior design.
The Supply Chain (SC) part of this thesis studies complex products from SC perspective. In this part, the set of firms operating to develop a complex product have been considered as a complex Supply Network (SN). The inefficiency of these SNs has not received adequate attention from scholars and its inefficacy has not been studied. Taking a CAS view, a network game model of complex SNs is developed in this dissertation. In this model, firms as agents make decisions on their level on integration with and differentiation from their neighbour firms. Their decisions are mainly myopic optimizing their local performance rather than the SN performance. To study inefficiency of SNs, a decision space is defined and examined. The decision space in which firms’ integration decisions more than their myopic firm-level integration improves the SN performance is considered as a SN inefficiency measure.
A set of experiments are conducted on the PD model. The experiments compare the PD performance of the two misalignment forms. They also examine the PD performance effects of misalignments at different hierarchical levels. The results of the Type I and Type II experiments demonstrate completely different patterns. According to these results, some possible PD strategies to manage the PD performance effects of misalignment are identified.
By analysis of the SN model, the SN part of this thesis identifies some strategies that managers can apply to manage their SN inefficiency. The interactions pattern and hierarchy in the complex SN are…
Subjects/Keywords: complex products; product development; supply network; hierarchy; complex adaptive systems
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APA (6th Edition):
Songhori, M. J. (2013). Modeling of complex product development and complex supply networks. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/39790
Chicago Manual of Style (16th Edition):
Songhori, Mohsen Jafari. “Modeling of complex product development and complex supply networks.” 2013. Doctoral Dissertation, University of Melbourne. Accessed April 18, 2021.
http://hdl.handle.net/11343/39790.
MLA Handbook (7th Edition):
Songhori, Mohsen Jafari. “Modeling of complex product development and complex supply networks.” 2013. Web. 18 Apr 2021.
Vancouver:
Songhori MJ. Modeling of complex product development and complex supply networks. [Internet] [Doctoral dissertation]. University of Melbourne; 2013. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/11343/39790.
Council of Science Editors:
Songhori MJ. Modeling of complex product development and complex supply networks. [Doctoral Dissertation]. University of Melbourne; 2013. Available from: http://hdl.handle.net/11343/39790
Universiteit Utrecht
19.
Hermans, D.M.H.
A learning journey; A study on the development of interactions, trust and learning in a global climate network.
Degree: 2010, Universiteit Utrecht
URL: http://dspace.library.uu.nl:8080/handle/1874/187272
► This study describes and analyses the development of a global climate network case: the 2020 Climate Solutions Meshwork. Meshwork is initiated as a new type…
(more)
▼ This study describes and analyses the development of a global climate
network case: the 2020 Climate Solutions Meshwork. Meshwork is initiated as a new type of
network with the intention to create improved collaboration and learning. Development between initiation in December 2009 and May 2010 show that initially, face-to-face interactions on a global climate summit in Copenhagen create increased numbers of actors as well as high levels of trust and learning among participants.
Network management actively facilitates these interactions and is viewed as positively correlated to high trust and learning levels. Afterwards, the accompanied online platform is put in place but online facilitation does not take place. Trust and learning levels among participants decrease, even though new members are coming in to a small extent. In May 2010, with 1500 participants, interactions are scarce and collaboration is minimal, only among a handful of actors. Online colaboration on climate issues in a
network based on voluntary interdependencies has proven a difficult endeavour to achieve. Nonetheless,
network management (the Meshwork team) is aware of difficulties in development and actively centralizes a learning attitude towards improving Meshworking practices in the future.
Advisors/Committee Members: Klijn, E.H..
Subjects/Keywords: network; interactions; development; trust; learning; actors; complex; network management
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APA (6th Edition):
Hermans, D. M. H. (2010). A learning journey; A study on the development of interactions, trust and learning in a global climate network. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/187272
Chicago Manual of Style (16th Edition):
Hermans, D M H. “A learning journey; A study on the development of interactions, trust and learning in a global climate network.” 2010. Masters Thesis, Universiteit Utrecht. Accessed April 18, 2021.
http://dspace.library.uu.nl:8080/handle/1874/187272.
MLA Handbook (7th Edition):
Hermans, D M H. “A learning journey; A study on the development of interactions, trust and learning in a global climate network.” 2010. Web. 18 Apr 2021.
Vancouver:
Hermans DMH. A learning journey; A study on the development of interactions, trust and learning in a global climate network. [Internet] [Masters thesis]. Universiteit Utrecht; 2010. [cited 2021 Apr 18].
Available from: http://dspace.library.uu.nl:8080/handle/1874/187272.
Council of Science Editors:
Hermans DMH. A learning journey; A study on the development of interactions, trust and learning in a global climate network. [Masters Thesis]. Universiteit Utrecht; 2010. Available from: http://dspace.library.uu.nl:8080/handle/1874/187272
McMaster University
20.
Sheikh Alzoor, Fayez.
Multi-Scale Classification of Ontario Highway Infrastructure: A Network Theoretic Approach to Guide Bridge Rehabilitation Strategy.
Degree: MASc, 2018, McMaster University
URL: http://hdl.handle.net/11375/23053
► Highway bridges are among the most vulnerable and expensive components in transportation networks. In response, the Government of Ontario has allocated $26 billion in the…
(more)
▼ Highway bridges are among the most vulnerable and expensive components in transportation networks. In response, the Government of Ontario has allocated $26 billion in the next 10 years to address issues pertaining to aging bridge and deteriorating highway infrastructure in the province. Although several approaches have been developed to guide their rehabilitation, most bridge rehabilitation approaches are focused on the component level (individual bridge) in a relative isolation of other bridges in the network. The current study utilizes a complex network theoretic approach to quantify the topological characteristics of the Ontario Bridge Network (OBN) and subsequently evaluate the OBN robustness and vulnerability characteristics. These measures are then integrated in the development of a Multi Scale Bridge Classification (MSBC) approach—an innovative classification approach that links the OBN component level data (i.e., Bridge Condition Index and year of construction, etc.) to the corresponding dynamic network-level measures. The novel approach calls for a paradigm shift in the strategy governing classifying and prioritizing bridge rehabilitation projects based on bridge criticality within the entire network, rather than only the individual bridge’s structural conditions. The model was also used to identify the most critical bridges in the OBN under different disruptions to facilitate rapid implementation of the study results.
Thesis
Master of Applied Science (MASc)
Advisors/Committee Members: Mohamed, Moataz, El-Dakhakhni, Wael, Civil Engineering.
Subjects/Keywords: Bridge Rehabilitation; Complex Network Theory; Network Topology; Robustness; Vulnerability Index
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APA (6th Edition):
Sheikh Alzoor, F. (2018). Multi-Scale Classification of Ontario Highway Infrastructure: A Network Theoretic Approach to Guide Bridge Rehabilitation Strategy. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/23053
Chicago Manual of Style (16th Edition):
Sheikh Alzoor, Fayez. “Multi-Scale Classification of Ontario Highway Infrastructure: A Network Theoretic Approach to Guide Bridge Rehabilitation Strategy.” 2018. Masters Thesis, McMaster University. Accessed April 18, 2021.
http://hdl.handle.net/11375/23053.
MLA Handbook (7th Edition):
Sheikh Alzoor, Fayez. “Multi-Scale Classification of Ontario Highway Infrastructure: A Network Theoretic Approach to Guide Bridge Rehabilitation Strategy.” 2018. Web. 18 Apr 2021.
Vancouver:
Sheikh Alzoor F. Multi-Scale Classification of Ontario Highway Infrastructure: A Network Theoretic Approach to Guide Bridge Rehabilitation Strategy. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/11375/23053.
Council of Science Editors:
Sheikh Alzoor F. Multi-Scale Classification of Ontario Highway Infrastructure: A Network Theoretic Approach to Guide Bridge Rehabilitation Strategy. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/23053
McMaster University
21.
Yassien, Yassien.
AIR TRANSPORTATION INFRASTRUCTURE ROBUSTNESS ASSESSMENT FOR PROACTIVE SYSTEMIC RISK MANAGEMENT.
Degree: MASc, 2020, McMaster University
URL: http://hdl.handle.net/11375/25410
► A key attribute of resilience, robustness serves as a predictor of infrastructure system performance under disruptions, thus informing proactive infrastructure risk management. A literature review…
(more)
▼ A key attribute of resilience, robustness serves as a predictor of infrastructure system performance under disruptions, thus informing proactive infrastructure risk management. A literature review indicated that previous studies did not consider some key factors that can influence the robustness of Air Transportation Infrastructure Networks (ATIN) and thus their (system-level cascade) systemic risk management processes. In this respect, the current study first assesses existing and then develops a new methodology to quantify the robustness of ATIN. Specifically, based on integrating travel time and flight frequency, the study develops alternative best route and link weight approaches to assess key ATIN robustness measures and relevant operating cost losses (OCL). In order to demonstrate the practical use of the developed methodology, the robustness and the associated OCL of the Canadian Domestic Air Traffic Network are evaluated under random failures (i.e., disruptive events that occur randomly) and targeted threats (i.e., disruptive events that occur deliberately). The analysis results show that the network robustness is influenced by the utilized evaluation approach, especially after 20% of the network components become nonoperational. Overall, the methodology developed within this study is expected to provide ATIN policymakers with the means to quantify the network robustness and OCL, and thus enable ATIN resilience-guided proactive risk management in the face of natural or anthropogenic hazard realizations.
Thesis
Master of Applied Science (MASc)
Advisors/Committee Members: El-Dakhakhni, Wael, Mohamed, Moataz, Civil Engineering.
Subjects/Keywords: Air traffic network; Complex Network Theory; Proactive Risk Management; Resilience; Robustness
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APA (6th Edition):
Yassien, Y. (2020). AIR TRANSPORTATION INFRASTRUCTURE ROBUSTNESS ASSESSMENT FOR PROACTIVE SYSTEMIC RISK MANAGEMENT. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/25410
Chicago Manual of Style (16th Edition):
Yassien, Yassien. “AIR TRANSPORTATION INFRASTRUCTURE ROBUSTNESS ASSESSMENT FOR PROACTIVE SYSTEMIC RISK MANAGEMENT.” 2020. Masters Thesis, McMaster University. Accessed April 18, 2021.
http://hdl.handle.net/11375/25410.
MLA Handbook (7th Edition):
Yassien, Yassien. “AIR TRANSPORTATION INFRASTRUCTURE ROBUSTNESS ASSESSMENT FOR PROACTIVE SYSTEMIC RISK MANAGEMENT.” 2020. Web. 18 Apr 2021.
Vancouver:
Yassien Y. AIR TRANSPORTATION INFRASTRUCTURE ROBUSTNESS ASSESSMENT FOR PROACTIVE SYSTEMIC RISK MANAGEMENT. [Internet] [Masters thesis]. McMaster University; 2020. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/11375/25410.
Council of Science Editors:
Yassien Y. AIR TRANSPORTATION INFRASTRUCTURE ROBUSTNESS ASSESSMENT FOR PROACTIVE SYSTEMIC RISK MANAGEMENT. [Masters Thesis]. McMaster University; 2020. Available from: http://hdl.handle.net/11375/25410
University of Colorado
22.
Larremore, Daniel Benjamin.
Critical Dynamics in Complex Excitable Networks.
Degree: PhD, Applied Mathematics, 2012, University of Colorado
URL: https://scholar.colorado.edu/appm_gradetds/28
► We study the effect of network structure on the dynamical response of networks of coupled discrete-state excitable elements to two distinct types of stimulus.…
(more)
▼ We study the effect of
network structure on the dynamical response of networks of coupled discrete-state excitable elements to two distinct types of stimulus. First, we consider networks which are stochastically stimulated by an external source. Such systems have been used as toy models for the dynamics of some human sensory neuronal networks and neuron cultures. The collective dynamics of such systems depends on the topology of the connections in the
network. Here we develop a general theoretical approach to study the effects of
network topology on dynamic range, which quantifies the range of stimulus intensities resulting in distinguishable
network responses. We find that the largest eigenvalue of the weighted
network adjacency matrix governs the
network dynamic range. Specifically, a largest eigenvalue equal to one corresponds to a critical regime with maximum dynamic range. This result appears to hold for random, all-to-all, and scale free topologies, and is robust to the inclusion of time delays and refractory states. We gain deeper insight into the effects of
network topology using a nonlinear analysis in terms of additional spectral properties of the adjacency matrix. We find that homogeneous networks can reach a higher dynamic range than those with heterogeneous topology. Second, we consider networks stimulated only once at a single node, with dynamics allowed to evolve without additional stimulus. Each realization of such a process will create a cascade of activity of varying duration and size. We analyze the distributions of cascade size and duration in
complex networks resulting from a single nodal excitation, finding that when the largest eigenvalue is equal to one, so-called ``critical avalanches'' are power-law distributed in size, with exponent -3/2, and power-law distributed in duration, with exponent -2. We employ techniques from dynamical systems to recover the differences among avalanches started at different
network nodes, also deriving distributions for avalanches in subcritical and supercritical regimes.
Advisors/Committee Members: Juan G. Restrepo, James D. Meiss, Keith Julien.
Subjects/Keywords: avalanche; complex network; criticality; neural network; nonlinear dynamics; Applied Mathematics
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APA (6th Edition):
Larremore, D. B. (2012). Critical Dynamics in Complex Excitable Networks. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/28
Chicago Manual of Style (16th Edition):
Larremore, Daniel Benjamin. “Critical Dynamics in Complex Excitable Networks.” 2012. Doctoral Dissertation, University of Colorado. Accessed April 18, 2021.
https://scholar.colorado.edu/appm_gradetds/28.
MLA Handbook (7th Edition):
Larremore, Daniel Benjamin. “Critical Dynamics in Complex Excitable Networks.” 2012. Web. 18 Apr 2021.
Vancouver:
Larremore DB. Critical Dynamics in Complex Excitable Networks. [Internet] [Doctoral dissertation]. University of Colorado; 2012. [cited 2021 Apr 18].
Available from: https://scholar.colorado.edu/appm_gradetds/28.
Council of Science Editors:
Larremore DB. Critical Dynamics in Complex Excitable Networks. [Doctoral Dissertation]. University of Colorado; 2012. Available from: https://scholar.colorado.edu/appm_gradetds/28
Delft University of Technology
23.
Tundulyasaree, Krissada (author).
Topological characterizing and clustering of public transport networks.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:312177b0-6317-47ff-b3d7-c5ead5e2feb5
► Public transport networks (PTNs) have an impact on both travelers’ behavior and system operations. A meaningful approach to investigate PTNs is via their topological structure…
(more)
▼ Public transport networks (PTNs) have an impact on both travelers’ behavior and system operations. A meaningful approach to investigate PTNs is via their topological structure because it was found to be correlated to the operational performance, the total ridership, consumer experience, passenger flow distribution, and network resilience. A handful number of past studies characterized and compare PTNs structure, but little is known about broader or general classification worldwide. This study examined how PTNs can be clustered into groups when considering multiple features. Centralization, accessibility, robustness, service connectivity and directness are five main considered network features used in previous studies to analyze the network structure. K-means, hierarchical clustering and principal component analysis were performed to identify the cluster of PTNs defined from those five features. To illustrate the method, we conducted a case study of 20 real-life rail-bound networks worldwide generated by the up-to-date general transit feed specification (GTFS) data. As a result, we were able to identify four main meaningful clusters: tram, tram-related, metro and tram and mixed modes. Although modes of transportation and the size of the network were not parts of features, they heavily influence the clusters. The proposed method show automatic and reproducible tools to empirically identify topological patterns of PTNs.
Transport, Infrastructure and Logistics
Advisors/Committee Members: Cats, Oded (graduation committee), Snelder, Maaike (mentor), Huang, Yilin (graduation committee), Luo, Ding (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Public transport network; Topology; Graph theory; Complex Network Analysis; Clustering algorithms
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APA (6th Edition):
Tundulyasaree, K. (. (2019). Topological characterizing and clustering of public transport networks. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:312177b0-6317-47ff-b3d7-c5ead5e2feb5
Chicago Manual of Style (16th Edition):
Tundulyasaree, Krissada (author). “Topological characterizing and clustering of public transport networks.” 2019. Masters Thesis, Delft University of Technology. Accessed April 18, 2021.
http://resolver.tudelft.nl/uuid:312177b0-6317-47ff-b3d7-c5ead5e2feb5.
MLA Handbook (7th Edition):
Tundulyasaree, Krissada (author). “Topological characterizing and clustering of public transport networks.” 2019. Web. 18 Apr 2021.
Vancouver:
Tundulyasaree K(. Topological characterizing and clustering of public transport networks. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Apr 18].
Available from: http://resolver.tudelft.nl/uuid:312177b0-6317-47ff-b3d7-c5ead5e2feb5.
Council of Science Editors:
Tundulyasaree K(. Topological characterizing and clustering of public transport networks. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:312177b0-6317-47ff-b3d7-c5ead5e2feb5
Clemson University
24.
Ushijima-Mwesigwa, Hayato Montezuma.
Models for Networks with Consumable Resources: Applications to Smart Cities.
Degree: PhD, School of Computing, 2018, Clemson University
URL: https://tigerprints.clemson.edu/all_dissertations/2284
► In this dissertation, we introduce different models for understanding and controlling the spreading dynamics of a network with a consumable resource. In particular, we consider…
(more)
▼ In this dissertation, we introduce different models for understanding and controlling the spreading dynamics of a
network with a consumable resource. In particular, we consider a spreading process where a resource necessary for transit is partially consumed along the way while being refilled at special nodes on the
network. Examples include fuel consumption of vehicles together with refueling stations, information loss during dissemination with error correcting nodes, consumption of ammunition of military troops while moving, and migration of wild animals in a
network with a limited number of water-holes. We undertake this study from two different perspectives. First, we consider a
network science perspective where we are interested in identifying the influential nodes and estimating a nodes’ relative spreading influence in the
network. For this reason, we propose generalizations of the well-known centrality measures to model such a spreading process with consumable resources. Next, from an optimization perspective, we focus on the application of an Electric Vehicle road
network equipped with wireless charging lanes as a resource allocation problem. The objective is this case is to identify a set of nodes for optimal placement of the wireless charging lanes. For this reason, we propose an integer programming model formulation and use it as a building block for different realistic scenarios. We conclude this dissertation by giving an approach to improve route selection for the optimization model proposed by using feedback data while giving comparisons to different realistic scenarios.
Advisors/Committee Members: Ilya Safro, Committee Chair, Mashrur Chowdhury, Brian Dean, Feng Luo.
Subjects/Keywords: Centrality; Complex Networks; Graph Algorithms; Integer Programming; Network Analysis; Network Science
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APA (6th Edition):
Ushijima-Mwesigwa, H. M. (2018). Models for Networks with Consumable Resources: Applications to Smart Cities. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2284
Chicago Manual of Style (16th Edition):
Ushijima-Mwesigwa, Hayato Montezuma. “Models for Networks with Consumable Resources: Applications to Smart Cities.” 2018. Doctoral Dissertation, Clemson University. Accessed April 18, 2021.
https://tigerprints.clemson.edu/all_dissertations/2284.
MLA Handbook (7th Edition):
Ushijima-Mwesigwa, Hayato Montezuma. “Models for Networks with Consumable Resources: Applications to Smart Cities.” 2018. Web. 18 Apr 2021.
Vancouver:
Ushijima-Mwesigwa HM. Models for Networks with Consumable Resources: Applications to Smart Cities. [Internet] [Doctoral dissertation]. Clemson University; 2018. [cited 2021 Apr 18].
Available from: https://tigerprints.clemson.edu/all_dissertations/2284.
Council of Science Editors:
Ushijima-Mwesigwa HM. Models for Networks with Consumable Resources: Applications to Smart Cities. [Doctoral Dissertation]. Clemson University; 2018. Available from: https://tigerprints.clemson.edu/all_dissertations/2284
University of North Texas
25.
Hollingshad, Nicholas W.
A Non-equilibrium Approach to Scale Free Networks.
Degree: 2012, University of North Texas
URL: https://digital.library.unt.edu/ark:/67531/metadc149609/
► Many processes and systems in nature and society can be characterized as large numbers of discrete elements that are (usually non-uniformly) interrelated. These networks were…
(more)
▼ Many processes and systems in nature and society can be characterized as large numbers of discrete elements that are (usually non-uniformly) interrelated. These networks were long thought to be random, but in the late 1990s, Barabási and Albert found that an underlying structure did in fact exist in many natural and technological networks that are now referred to as scale free. Since then, researchers have gained a much deeper understanding of this particular form of complexity, largely by combining graph theory, statistical physics, and advances in computing technology. This dissertation focuses on out-of-equilibrium dynamic processes as they unfold on these
complex networks. Diffusion in networks of non-interacting nodes is shown to be temporally
complex, while equilibrium is represented by a stable state with Poissonian fluctuations. Scale free networks achieve equilibrium very quickly compared to regular networks, and the most efficient are those with the lowest inverse power law exponent. Temporally
complex diffusion also occurs in networks with interacting nodes under a cooperative decision-making model. At a critical value of the cooperation parameter, the most efficient scale free
network achieves consensus almost as quickly as the equivalent all-to-all
network. This finding suggests that the ubiquity of scale free networks in nature is due to Zipf's principle of least effort. It also suggests that an efficient scale free
network structure may be optimal for real networks that require high connectivity but are hampered by high link costs.
Advisors/Committee Members: Grigolini, Paolo, Gross, Gunter, Krokhin, Arkadii, Mikler, Armin R..
Subjects/Keywords: Complexity; complex networks; scale free networks; network efficiency; networks; network dynamics
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26.
Badepalli, Satish.
Network Behavior in Thin Film Growth Dynamics.
Degree: 2017, University of Nevada – Reno
URL: http://hdl.handle.net/11714/2029
► Understanding patterns and components in thin film growth is crucial for many engineering applications. Further, the growth dynamics (e.g., shadowing and re-emission effects) of thin…
(more)
▼ Understanding patterns and components in thin film growth is crucial for many engineering applications. Further, the growth dynamics (e.g., shadowing and re-emission effects) of thin films exist in several other natural and man-made phenomena. Recent work developed
network science techniques to study the growth dynamics of thin films and nanostructures. These efforts used a grid
network model (i.e. viewing of each point on the thin film as an intersection point of a grid) via Monte Carlo simulation methods to study the shadowing and re-emission effects in the growth. These effects are crucial in understanding the relationships between growth dynamics and the resulting structural properties of the film to be grown. In this dissertation, we use a cluster-based
network model with Monte Carlo simulation method to study these effects in thin film growth. We use image processing to identify clusters of points on the film and establish a
network model of these clusters. Monte Carlo simulations are used to grow films and dynamically track the trajectories of re-emitted particles. We treat the points on the film substrate and cluster formations from the deposition of adatoms / particles on the surface of the substrate as the nodes of
network, and movement of particles between these points or clusters as the traffic of the
network. Then, graph theory is used to study various
network statistics and characteristics that would explain various important phenomena in the thin film growth. We compare the cluster-based results with the grid-based results to determine which method is better suited to study the underlying characteristics of the thin film. Based on the clusters and the points on the substrate, we also develop a
network traffic model to study the characteristics and phenomena like fractal behavior in the count and inter-arrival time of the particles. Our results show that the
network theory of the growth process explains some of the underlying phenomena in film growth better than the existing theoretical and statistical models.
Advisors/Committee Members: Yuksel, Murat (advisor), Bebis, George (committee member), Dascalu, Sergiu (committee member), Gunes, Mehmet (committee member), Chandra, Dhanesh (committee member).
Subjects/Keywords: Complex Networks; Network Science; Network Traffic; Thin Film Growth
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Badepalli, S. (2017). Network Behavior in Thin Film Growth Dynamics. (Thesis). University of Nevada – Reno. Retrieved from http://hdl.handle.net/11714/2029
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Badepalli, Satish. “Network Behavior in Thin Film Growth Dynamics.” 2017. Thesis, University of Nevada – Reno. Accessed April 18, 2021.
http://hdl.handle.net/11714/2029.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Badepalli, Satish. “Network Behavior in Thin Film Growth Dynamics.” 2017. Web. 18 Apr 2021.
Vancouver:
Badepalli S. Network Behavior in Thin Film Growth Dynamics. [Internet] [Thesis]. University of Nevada – Reno; 2017. [cited 2021 Apr 18].
Available from: http://hdl.handle.net/11714/2029.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Badepalli S. Network Behavior in Thin Film Growth Dynamics. [Thesis]. University of Nevada – Reno; 2017. Available from: http://hdl.handle.net/11714/2029
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
27.
Li, Ziyu (author).
Diffusion based temporal network embedding for link prediction.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:5efbbf13-76b0-4fcf-848c-a7971c92e499
► Link prediction in complex networks has attracted increasing attention. The link prediction algorithms can be used to retrieve missing information, identify spurious interactions, capturing net-…
(more)
▼ Link prediction in
complex networks has attracted increasing attention. The link prediction algorithms can be used to retrieve missing information, identify spurious interactions, capturing net- work evolution, and so on. Recently,
network embedding has been proposed as a new strategy to embed
network into low-dimensional vector space. By embedding nodes into vectors, the link pre- diction problem can be converted into a similarity comparison task. Nodes with similar vectors are more likely to connect. Some traditional
network embedding methods include matrix factorization, random walk paradigm and deep neural
network models. In this thesis, we propose SISNE, a diffusion based paradigm for node embedding, applying Susceptible-Infected-Susceptible (SIS) model to extract node neighborhood structure. Both random walk based algorithms and our proposed method sample node sequences as input and feed them into a Skip-gram model, a representative language model that embeds words into vectors. Specially, our proposed model provides flexibility to explore the
network topology by operating in- formation spreading on networks. Another contribution of the proposed model is that SISNE takes into the account of the evolving nature of
complex networks. To verify the efficacy, we conduct experiments on missing link prediction task and show that our SIS diffusion based model outperforms other state-of-the-art
network embedding algorithms across all four empirical datasets, reaching a maximal 7% improvement. Importantly, even when the input size is small, the performance remains stable whereas other baseline models drop dramatically, which indicates that our proposed model is less sensitive to input size and suggests that the model is applicable to large-scale networks. Moreover, we further show that as long as the infection probability β is larger than the threshold value of the diffusion model, we can obtain a relatively high performance for link prediction task. Taken together, our work has shown great effectiveness and efficiency in learning embeddings in temporal networks.
Advisors/Committee Members: Wang, Huijuan (mentor), Zhan, Xiuxiu (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Temporal Network Embedding; Link Prediction; Complex Network Analysis; Information Spreading
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Li, Z. (. (2019). Diffusion based temporal network embedding for link prediction. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5efbbf13-76b0-4fcf-848c-a7971c92e499
Chicago Manual of Style (16th Edition):
Li, Ziyu (author). “Diffusion based temporal network embedding for link prediction.” 2019. Masters Thesis, Delft University of Technology. Accessed April 18, 2021.
http://resolver.tudelft.nl/uuid:5efbbf13-76b0-4fcf-848c-a7971c92e499.
MLA Handbook (7th Edition):
Li, Ziyu (author). “Diffusion based temporal network embedding for link prediction.” 2019. Web. 18 Apr 2021.
Vancouver:
Li Z(. Diffusion based temporal network embedding for link prediction. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Apr 18].
Available from: http://resolver.tudelft.nl/uuid:5efbbf13-76b0-4fcf-848c-a7971c92e499.
Council of Science Editors:
Li Z(. Diffusion based temporal network embedding for link prediction. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:5efbbf13-76b0-4fcf-848c-a7971c92e499
University of Manchester
28.
Hernández DomÃnguez, José Luis.
BUILDING MODELS OF SMALL DNA CONTROL ELEMENTS FOR
PREDICTION OF TRANSCRIPTION FACTOR ACTIVITY.
Degree: 2020, University of Manchester
URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323566
► Transcription factors are proteins that play a key role in the control of gene transcription in cells. Understanding how this control works would be of…
(more)
▼ Transcription factors are proteins that play a key
role in the control of gene transcription in cells. Understanding
how this control works would be of immense value in many areas of
biology and medicine. However, the transcription control process is
complex, and transcription factors are only a part of a much more
complex and dynamic system. This system is noisy and poorly
understood, with numerous components. The focus of this thesis is
specifically on the regulation of the transcription factors. We
narrowed down this focus to only include in the system's model the
transcriptional control of the regulation of transcription factors
by themselves. We omit from this research other aspects of the
process, such as miRNA. Our main research question is centred on
determining how much of the observed cellular behaviour can be
modelled with this simplified system. The regulation of
transcription factors by transcription factors can be expressed as
a
network. We made a realistic
network based on real world data
obtained from TRANSFAC and TRRUST, two databases that describe the
interactions between transcription factors and their target
proteins for the human model. Furthermore, we applied the same
model in yeast to corroborate the results, basing the
network on
the YEASTRACT database. The mathematical modelling of the system
takes the model proposed by Han in 2013, and applies it to the
networks. We modified this model to take into account the fact that
these processes occur inside the cell nucleus, and are therefore
constrained by the small size and finite quality of the nucleus.
The model validity was tested by exploring how the
network responds
when the transcription factors are perturbed, and by comparing this
with real world data from when transcription factors change,
specifically in cancers and rare genetic diseases. As part of these
processes, we developed two models for measuring the impact given a
perturbation of a
network. The first model is based on the
Euclidean distance between the original and the perturbed
network,
while the second model uses the topological characteristics of the
network to predict the impact of the perturbation. We further
analyse the impact,
network topological characteristics, and a
measure of
network centrality. Interestingly, we found significant
correlations between the behaviour of the model system with the
data from cancer and rare genetic diseases. This has provided
support to the adequacy of our simplified model. Moreover, the
yeast model also presented significant correlation of the behaviour
of the model system with phenotypes. In building the networks, we
observed that there were significant examples of transcription
factor auto-regulation (which we call self-loops in the
network
model) in both models. Such autoregulation has been extensively
studied mathematically in systems control theory, and biologically
in prokaryotic systems, with fewer studies in eukaryotes. From the
model analysis, we were able to develop new theories related to the
importance of these self-loops in…
Advisors/Committee Members: NAVARRO LOPEZ, EVA EM, Brass, Andrew, Navarro Lopez, Eva.
Subjects/Keywords: Transcription Factors; Regulatory network; Complex Networks; Transcription factors network; Basal regulatory network
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hernández DomÃnguez, J. L. (2020). BUILDING MODELS OF SMALL DNA CONTROL ELEMENTS FOR
PREDICTION OF TRANSCRIPTION FACTOR ACTIVITY. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323566
Chicago Manual of Style (16th Edition):
Hernández DomÃnguez, José Luis. “BUILDING MODELS OF SMALL DNA CONTROL ELEMENTS FOR
PREDICTION OF TRANSCRIPTION FACTOR ACTIVITY.” 2020. Doctoral Dissertation, University of Manchester. Accessed April 18, 2021.
http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323566.
MLA Handbook (7th Edition):
Hernández DomÃnguez, José Luis. “BUILDING MODELS OF SMALL DNA CONTROL ELEMENTS FOR
PREDICTION OF TRANSCRIPTION FACTOR ACTIVITY.” 2020. Web. 18 Apr 2021.
Vancouver:
Hernández DomÃnguez JL. BUILDING MODELS OF SMALL DNA CONTROL ELEMENTS FOR
PREDICTION OF TRANSCRIPTION FACTOR ACTIVITY. [Internet] [Doctoral dissertation]. University of Manchester; 2020. [cited 2021 Apr 18].
Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323566.
Council of Science Editors:
Hernández DomÃnguez JL. BUILDING MODELS OF SMALL DNA CONTROL ELEMENTS FOR
PREDICTION OF TRANSCRIPTION FACTOR ACTIVITY. [Doctoral Dissertation]. University of Manchester; 2020. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:323566
University of Manchester
29.
Hernández Domínguez, José.
Building models of small DNA control elements for prediction of transcription factor activity.
Degree: PhD, 2020, University of Manchester
URL: https://www.research.manchester.ac.uk/portal/en/theses/building-models-of-small-dna-control-elements-for-prediction-of-transcription-factor-activity(2981a760-d8f8-4d0b-8669-ec69e5eb56cb).html
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799497
► Transcription factors are proteins that play a key role in the control of gene transcription in cells. Understanding how this control works would be of…
(more)
▼ Transcription factors are proteins that play a key role in the control of gene transcription in cells. Understanding how this control works would be of immense value in many areas of biology and medicine. However, the transcription control process is complex, and transcription factors are only a part of a much more complex and dynamic system. This system is noisy and poorly understood, with numerous components. The focus of this thesis is specifically on the regulation of the transcription factors. We narrowed down this focus to only include in the system's model the transcriptional control of the regulation of transcription factors by themselves. We omit from this research other aspects of the process, such as miRNA. Our main research question is centred on determining how much of the observed cellular behaviour can be modelled with this simplified system. The regulation of transcription factors by transcription factors can be expressed as a network. We made a realistic network based on real world data obtained from TRANSFAC and TRRUST, two databases that describe the interactions between transcription factors and their target proteins for the human model. Furthermore, we applied the same model in yeast to corroborate the results, basing the network on the YEASTRACT database. The mathematical modelling of the system takes the model proposed by Han in 2013, and applies it to the networks. We modified this model to take into account the fact that these processes occur inside the cell nucleus, and are therefore constrained by the small size and finite quality of the nucleus. The model validity was tested by exploring how the network responds when the transcription factors are perturbed, and by comparing this with real world data from when transcription factors change, specifically in cancers and rare genetic diseases. As part of these processes, we developed two models for measuring the impact given a perturbation of a network. The first model is based on the Euclidean distance between the original and the perturbed network, while the second model uses the topological characteristics of the network to predict the impact of the perturbation. We further analyse the impact, network topological characteristics, and a measure of network centrality. Interestingly, we found significant correlations between the behaviour of the model system with the data from cancer and rare genetic diseases. This has provided support to the adequacy of our simplified model. Moreover, the yeast model also presented significant correlation of the behaviour of the model system with phenotypes. In building the networks, we observed that there were significant examples of transcription factor auto-regulation (which we call self-loops in the network model) in both models. Such autoregulation has been extensively studied mathematically in systems control theory, and biologically in prokaryotic systems, with fewer studies in eukaryotes. From the model analysis, we were able to develop new theories related to the importance of these self-loops in…
Subjects/Keywords: Transcription factors network; Complex Networks; Basal regulatory network; Transcription Factors; Regulatory network
Record Details
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Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hernández Domínguez, J. (2020). Building models of small DNA control elements for prediction of transcription factor activity. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/building-models-of-small-dna-control-elements-for-prediction-of-transcription-factor-activity(2981a760-d8f8-4d0b-8669-ec69e5eb56cb).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799497
Chicago Manual of Style (16th Edition):
Hernández Domínguez, José. “Building models of small DNA control elements for prediction of transcription factor activity.” 2020. Doctoral Dissertation, University of Manchester. Accessed April 18, 2021.
https://www.research.manchester.ac.uk/portal/en/theses/building-models-of-small-dna-control-elements-for-prediction-of-transcription-factor-activity(2981a760-d8f8-4d0b-8669-ec69e5eb56cb).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799497.
MLA Handbook (7th Edition):
Hernández Domínguez, José. “Building models of small DNA control elements for prediction of transcription factor activity.” 2020. Web. 18 Apr 2021.
Vancouver:
Hernández Domínguez J. Building models of small DNA control elements for prediction of transcription factor activity. [Internet] [Doctoral dissertation]. University of Manchester; 2020. [cited 2021 Apr 18].
Available from: https://www.research.manchester.ac.uk/portal/en/theses/building-models-of-small-dna-control-elements-for-prediction-of-transcription-factor-activity(2981a760-d8f8-4d0b-8669-ec69e5eb56cb).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799497.
Council of Science Editors:
Hernández Domínguez J. Building models of small DNA control elements for prediction of transcription factor activity. [Doctoral Dissertation]. University of Manchester; 2020. Available from: https://www.research.manchester.ac.uk/portal/en/theses/building-models-of-small-dna-control-elements-for-prediction-of-transcription-factor-activity(2981a760-d8f8-4d0b-8669-ec69e5eb56cb).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.799497
Central Queensland University
30.
Ding, Ke.
Studying delay effects on complex dynamical networks.
Degree: 2011, Central Queensland University
URL: http://hdl.cqu.edu.au/10018/920082
► "Complex networks have attracted increasing attention form many fields due to their theoretical importance and practical applications. Because there exist the limitations and constraints for…
(more)
▼ "Complex networks have attracted increasing attention form many fields due to their theoretical importance and practical applications. Because there exist the limitations and constraints for the speeds of transmission, a signal traveling from one node to the other node in a complex network usually suffers a time delay. This thesis is to introduce some complex dynamical network models with coupling delays and to study time delay effects on complex dynamical networks" – Abstract.
Subjects/Keywords: Complex networks; Transmission speeds; Complex dynamical network models; Coupling; Delays; 010204 Dynamical Systems in Applications
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ding, K. (2011). Studying delay effects on complex dynamical networks. (Thesis). Central Queensland University. Retrieved from http://hdl.cqu.edu.au/10018/920082
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Ding, Ke. “Studying delay effects on complex dynamical networks.” 2011. Thesis, Central Queensland University. Accessed April 18, 2021.
http://hdl.cqu.edu.au/10018/920082.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ding, Ke. “Studying delay effects on complex dynamical networks.” 2011. Web. 18 Apr 2021.
Vancouver:
Ding K. Studying delay effects on complex dynamical networks. [Internet] [Thesis]. Central Queensland University; 2011. [cited 2021 Apr 18].
Available from: http://hdl.cqu.edu.au/10018/920082.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ding K. Studying delay effects on complex dynamical networks. [Thesis]. Central Queensland University; 2011. Available from: http://hdl.cqu.edu.au/10018/920082
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
◁ [1] [2] [3] [4] [5] … [12] ▶
. 
Open Access Theses and Dissertations
