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163 total matches.

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Dates

- 2016 – 2020 (55)
- 2011 – 2015 (75)
- 2006 – 2010 (33)

Department

- Computer Science (19)
- Computer Science and Engineering (14)
- Informatique (13)

Degrees

- PhD (51)
- Docteur es (18)
- MS (10)

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Université Catholique de Louvain

1.
Demesmaeker, Florian.
* Graph* cube

Degree: 2017, Université Catholique de Louvain

URL: http://hdl.handle.net/2078.1/thesis:10691

►

Due to the availability of rich network data, *graph* *mining* techniques have been improved to handle the emergence of such heterogeneous data. Exploratory data analysis…
(more)

Subjects/Keywords: Pattern mining; Hypothesis testing; Graph mining; Graph cube; Data mining

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Demesmaeker, F. (2017). Graph cube mining. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/thesis:10691

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Demesmaeker, Florian. “Graph cube mining.” 2017. Thesis, Université Catholique de Louvain. Accessed February 19, 2020. http://hdl.handle.net/2078.1/thesis:10691.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Demesmaeker, Florian. “Graph cube mining.” 2017. Web. 19 Feb 2020.

Vancouver:

Demesmaeker F. Graph cube mining. [Internet] [Thesis]. Université Catholique de Louvain; 2017. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/2078.1/thesis:10691.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Demesmaeker F. Graph cube mining. [Thesis]. Université Catholique de Louvain; 2017. Available from: http://hdl.handle.net/2078.1/thesis:10691

Not specified: Masters Thesis or Doctoral Dissertation

University of Hong Kong

2. Fang, Yixiang. Effective and efficient community search over large attributed graphs.

Degree: PhD, 2017, University of Hong Kong

URL: http://hdl.handle.net/10722/250721

► Communities, which are prevalent in attributed graphs such as social networks and knowledge bases, can be used in emerging applications such as product advertisement and…
(more)

Subjects/Keywords: Data mining; Graph algorithms

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APA (6^{th} Edition):

Fang, Y. (2017). Effective and efficient community search over large attributed graphs. (Doctoral Dissertation). University of Hong Kong. Retrieved from http://hdl.handle.net/10722/250721

Chicago Manual of Style (16^{th} Edition):

Fang, Yixiang. “Effective and efficient community search over large attributed graphs.” 2017. Doctoral Dissertation, University of Hong Kong. Accessed February 19, 2020. http://hdl.handle.net/10722/250721.

MLA Handbook (7^{th} Edition):

Fang, Yixiang. “Effective and efficient community search over large attributed graphs.” 2017. Web. 19 Feb 2020.

Vancouver:

Fang Y. Effective and efficient community search over large attributed graphs. [Internet] [Doctoral dissertation]. University of Hong Kong; 2017. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10722/250721.

Council of Science Editors:

Fang Y. Effective and efficient community search over large attributed graphs. [Doctoral Dissertation]. University of Hong Kong; 2017. Available from: http://hdl.handle.net/10722/250721

Anna University

3.
Sabeen S.
Association rule *mining* using directed graphs and
hypergraphs;.

Degree: Association rule mining using directed graphs and hypergraphs, 2015, Anna University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/40129

►

Data *mining* is a potential tool for prolific analysis of data The task of association rule *mining* in a large database is one of the…
(more)

Subjects/Keywords: Data mining; Directed graph model

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APA (6^{th} Edition):

S, S. (2015). Association rule mining using directed graphs and hypergraphs;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/40129

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

S, Sabeen. “Association rule mining using directed graphs and hypergraphs;.” 2015. Thesis, Anna University. Accessed February 19, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/40129.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

S, Sabeen. “Association rule mining using directed graphs and hypergraphs;.” 2015. Web. 19 Feb 2020.

Vancouver:

S S. Association rule mining using directed graphs and hypergraphs;. [Internet] [Thesis]. Anna University; 2015. [cited 2020 Feb 19]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/40129.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

S S. Association rule mining using directed graphs and hypergraphs;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/40129

Not specified: Masters Thesis or Doctoral Dissertation

Louisiana State University

4.
Jose, Neha Clare.
Social Media Network Data *Mining* and Optimization.

Degree: MS, Computer Sciences, 2016, Louisiana State University

URL: etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024

► Many small social aid organizations could benefit from collaborating with other organizations on common causes, but may not have the necessary social relationships. We present…
(more)

Subjects/Keywords: Graph Analysis; Centrality; Data Mining

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APA (6^{th} Edition):

Jose, N. C. (2016). Social Media Network Data Mining and Optimization. (Masters Thesis). Louisiana State University. Retrieved from etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024

Chicago Manual of Style (16^{th} Edition):

Jose, Neha Clare. “Social Media Network Data Mining and Optimization.” 2016. Masters Thesis, Louisiana State University. Accessed February 19, 2020. etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024.

MLA Handbook (7^{th} Edition):

Jose, Neha Clare. “Social Media Network Data Mining and Optimization.” 2016. Web. 19 Feb 2020.

Vancouver:

Jose NC. Social Media Network Data Mining and Optimization. [Internet] [Masters thesis]. Louisiana State University; 2016. [cited 2020 Feb 19]. Available from: etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024.

Council of Science Editors:

Jose NC. Social Media Network Data Mining and Optimization. [Masters Thesis]. Louisiana State University; 2016. Available from: etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024

5. Zhang, Yao. Optimizing and Understanding Network Structure for Diffusion.

Degree: PhD, Computer Science, 2017, Virginia Tech

URL: http://hdl.handle.net/10919/79674

► Given a population contact network and electronic medical records of patients, how to distribute vaccines to individuals to effectively control a flu epidemic? Similarly, given…
(more)

Subjects/Keywords: Data Mining; Graph/Network; Diffusion

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APA (6^{th} Edition):

Zhang, Y. (2017). Optimizing and Understanding Network Structure for Diffusion. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/79674

Chicago Manual of Style (16^{th} Edition):

Zhang, Yao. “Optimizing and Understanding Network Structure for Diffusion.” 2017. Doctoral Dissertation, Virginia Tech. Accessed February 19, 2020. http://hdl.handle.net/10919/79674.

MLA Handbook (7^{th} Edition):

Zhang, Yao. “Optimizing and Understanding Network Structure for Diffusion.” 2017. Web. 19 Feb 2020.

Vancouver:

Zhang Y. Optimizing and Understanding Network Structure for Diffusion. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10919/79674.

Council of Science Editors:

Zhang Y. Optimizing and Understanding Network Structure for Diffusion. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/79674

Rutgers University

6. Kang, Yunyi, 1991-. Anomaly detection in network using non-negative matrix factorization techniques.

Degree: MS, Industrial and Systems Engineering, 2015, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/48536/

► Anomaly detection is becoming an important problem in *graph* *mining*. This is because people are eager to find out unusual objects or patterns in a…
(more)

Subjects/Keywords: Graph theory; Anomalies; Data mining

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APA (6^{th} Edition):

Kang, Yunyi, 1. (2015). Anomaly detection in network using non-negative matrix factorization techniques. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/48536/

Chicago Manual of Style (16^{th} Edition):

Kang, Yunyi, 1991-. “Anomaly detection in network using non-negative matrix factorization techniques.” 2015. Masters Thesis, Rutgers University. Accessed February 19, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/48536/.

MLA Handbook (7^{th} Edition):

Kang, Yunyi, 1991-. “Anomaly detection in network using non-negative matrix factorization techniques.” 2015. Web. 19 Feb 2020.

Vancouver:

Kang, Yunyi 1. Anomaly detection in network using non-negative matrix factorization techniques. [Internet] [Masters thesis]. Rutgers University; 2015. [cited 2020 Feb 19]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48536/.

Council of Science Editors:

Kang, Yunyi 1. Anomaly detection in network using non-negative matrix factorization techniques. [Masters Thesis]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48536/

IUPUI

7.
Mandal, Aritra.
Distributed *graph* decomposition algorithms on Apache Spark.

Degree: 2018, IUPUI

URL: http://hdl.handle.net/1805/16924

►

Indiana University-Purdue University Indianapolis (IUPUI)

Structural analysis and *mining* of large and complex graphs for describing the characteristics of a vertex or an edge in…
(more)

Subjects/Keywords: Graph Mining; Big Data; Apache Spark; Graph Decomposition; Graph Partitioning; Clustering

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APA (6^{th} Edition):

Mandal, A. (2018). Distributed graph decomposition algorithms on Apache Spark. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/16924

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Mandal, Aritra. “Distributed graph decomposition algorithms on Apache Spark.” 2018. Thesis, IUPUI. Accessed February 19, 2020. http://hdl.handle.net/1805/16924.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Mandal, Aritra. “Distributed graph decomposition algorithms on Apache Spark.” 2018. Web. 19 Feb 2020.

Vancouver:

Mandal A. Distributed graph decomposition algorithms on Apache Spark. [Internet] [Thesis]. IUPUI; 2018. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/1805/16924.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mandal A. Distributed graph decomposition algorithms on Apache Spark. [Thesis]. IUPUI; 2018. Available from: http://hdl.handle.net/1805/16924

Not specified: Masters Thesis or Doctoral Dissertation

University of Alberta

8.
Simoes Gomes, Carolina.
Heavyweight Pattern *Mining* in Attributed Flow Graphs.

Degree: MS, Department of Computing Science, 2012, University of Alberta

URL: https://era.library.ualberta.ca/files/r494vk17h

► Flow graphs are an abstraction used to represent elements travelling through a network of nodes. The paths between nodes are directed edges in the *graph*,…
(more)

Subjects/Keywords: software analysis; pattern mining; program analysis; flow graph; program profiling; sub-graph mining; data mining

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APA (6^{th} Edition):

Simoes Gomes, C. (2012). Heavyweight Pattern Mining in Attributed Flow Graphs. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/r494vk17h

Chicago Manual of Style (16^{th} Edition):

Simoes Gomes, Carolina. “Heavyweight Pattern Mining in Attributed Flow Graphs.” 2012. Masters Thesis, University of Alberta. Accessed February 19, 2020. https://era.library.ualberta.ca/files/r494vk17h.

MLA Handbook (7^{th} Edition):

Simoes Gomes, Carolina. “Heavyweight Pattern Mining in Attributed Flow Graphs.” 2012. Web. 19 Feb 2020.

Vancouver:

Simoes Gomes C. Heavyweight Pattern Mining in Attributed Flow Graphs. [Internet] [Masters thesis]. University of Alberta; 2012. [cited 2020 Feb 19]. Available from: https://era.library.ualberta.ca/files/r494vk17h.

Council of Science Editors:

Simoes Gomes C. Heavyweight Pattern Mining in Attributed Flow Graphs. [Masters Thesis]. University of Alberta; 2012. Available from: https://era.library.ualberta.ca/files/r494vk17h

Washington State University

9. [No author]. Supervised Learning in Dynamic Streaming Graphs .

Degree: 2016, Washington State University

URL: http://hdl.handle.net/2376/12160

► With the emergence of networked data, *graph* classification has received considerable interest during the past years. Most approaches to *graph* classification focus on designing effective…
(more)

Subjects/Keywords: Computer science; Artificial intelligence; Dynamic graph; Graph classification; Graph kernel; Graph mining; Incremental learning

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APA (6^{th} Edition):

author], [. (2016). Supervised Learning in Dynamic Streaming Graphs . (Thesis). Washington State University. Retrieved from http://hdl.handle.net/2376/12160

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

author], [No. “Supervised Learning in Dynamic Streaming Graphs .” 2016. Thesis, Washington State University. Accessed February 19, 2020. http://hdl.handle.net/2376/12160.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

author], [No. “Supervised Learning in Dynamic Streaming Graphs .” 2016. Web. 19 Feb 2020.

Vancouver:

author] [. Supervised Learning in Dynamic Streaming Graphs . [Internet] [Thesis]. Washington State University; 2016. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/2376/12160.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. Supervised Learning in Dynamic Streaming Graphs . [Thesis]. Washington State University; 2016. Available from: http://hdl.handle.net/2376/12160

Not specified: Masters Thesis or Doctoral Dissertation

Louisiana State University

10.
Shakya, Shobhit Sandesh.
Pattern *Mining* and Events Discovery in Molecular Dynamics Simulations Data.

Degree: PhD, Computer Sciences, 2015, Louisiana State University

URL: etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034

► Molecular dynamics simulation method is widely used to calculate and understand a wide range of properties of materials. A lot of research efforts have been…
(more)

Subjects/Keywords: data mining; sub-graph mining; close path mining; molecular dynamics; pattern mining

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APA (6^{th} Edition):

Shakya, S. S. (2015). Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034

Chicago Manual of Style (16^{th} Edition):

Shakya, Shobhit Sandesh. “Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data.” 2015. Doctoral Dissertation, Louisiana State University. Accessed February 19, 2020. etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034.

MLA Handbook (7^{th} Edition):

Shakya, Shobhit Sandesh. “Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data.” 2015. Web. 19 Feb 2020.

Vancouver:

Shakya SS. Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data. [Internet] [Doctoral dissertation]. Louisiana State University; 2015. [cited 2020 Feb 19]. Available from: etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034.

Council of Science Editors:

Shakya SS. Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data. [Doctoral Dissertation]. Louisiana State University; 2015. Available from: etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034

Louisiana State University

11.
Basuchowdhuri, Partha.
Greedy methods for approximate *graph* matching with applications for social network analysis.

Degree: MSCS, Computer Sciences, 2009, Louisiana State University

URL: etd-04132009-134102 ; https://digitalcommons.lsu.edu/gradschool_theses/3105

► In this thesis, we study greedy algorithms for approximate sub-*graph* matching with attributed graphs. Such algorithms find one or multiple copies of a sub-*graph* pattern…
(more)

Subjects/Keywords: Sub-Graph Mining; Seed; Social Network Analysis

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APA (6^{th} Edition):

Basuchowdhuri, P. (2009). Greedy methods for approximate graph matching with applications for social network analysis. (Masters Thesis). Louisiana State University. Retrieved from etd-04132009-134102 ; https://digitalcommons.lsu.edu/gradschool_theses/3105

Chicago Manual of Style (16^{th} Edition):

Basuchowdhuri, Partha. “Greedy methods for approximate graph matching with applications for social network analysis.” 2009. Masters Thesis, Louisiana State University. Accessed February 19, 2020. etd-04132009-134102 ; https://digitalcommons.lsu.edu/gradschool_theses/3105.

MLA Handbook (7^{th} Edition):

Basuchowdhuri, Partha. “Greedy methods for approximate graph matching with applications for social network analysis.” 2009. Web. 19 Feb 2020.

Vancouver:

Basuchowdhuri P. Greedy methods for approximate graph matching with applications for social network analysis. [Internet] [Masters thesis]. Louisiana State University; 2009. [cited 2020 Feb 19]. Available from: etd-04132009-134102 ; https://digitalcommons.lsu.edu/gradschool_theses/3105.

Council of Science Editors:

Basuchowdhuri P. Greedy methods for approximate graph matching with applications for social network analysis. [Masters Thesis]. Louisiana State University; 2009. Available from: etd-04132009-134102 ; https://digitalcommons.lsu.edu/gradschool_theses/3105

12.
Zhao, Yuchen.
* Mining* Large Graphs.

Degree: 2013, University of Illinois – Chicago

URL: http://hdl.handle.net/10027/9955

► Recently, there is an increasing need for *mining* graphs with the rapidly growing social networks, Internet applications and communication networks. Among all these real-world applications,…
(more)

Subjects/Keywords: graph mining

…knowledge from *graph* structures to a number of fundamental
data *mining* tasks.
The *graph* structured… …*mining* techniques, the *graph* structures are complex and lack of
existing features in the *graph*… …flows, etc. There are
increasing needs for building models to *mining* *graph* data. In social… …cancers or chronic diseases (41; 84). Motivated by these challenges, *graph*
*mining* has… …*mining* problems by studying *graph* structures.
We first study using *graph* structures to…

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Zhao, Y. (2013). Mining Large Graphs. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/9955

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Zhao, Yuchen. “Mining Large Graphs.” 2013. Thesis, University of Illinois – Chicago. Accessed February 19, 2020. http://hdl.handle.net/10027/9955.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhao, Yuchen. “Mining Large Graphs.” 2013. Web. 19 Feb 2020.

Vancouver:

Zhao Y. Mining Large Graphs. [Internet] [Thesis]. University of Illinois – Chicago; 2013. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10027/9955.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhao Y. Mining Large Graphs. [Thesis]. University of Illinois – Chicago; 2013. Available from: http://hdl.handle.net/10027/9955

Not specified: Masters Thesis or Doctoral Dissertation

University of Victoria

13. Daneshmandmehrabani, Mahsa. Truss decomposition in large probabilistic graphs.

Degree: Department of Computer Science, 2019, University of Victoria

URL: http://hdl.handle.net/1828/11428

► Truss decomposition is an essential problem in *graph* *mining*, which focuses on discovering dense subgraphs of a *graph*. Detecting trusses in deterministic graphs is extensively…
(more)

Subjects/Keywords: Truss decomposition; graph mining; deterministic graphs

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APA (6^{th} Edition):

Daneshmandmehrabani, M. (2019). Truss decomposition in large probabilistic graphs. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/11428

Chicago Manual of Style (16^{th} Edition):

Daneshmandmehrabani, Mahsa. “Truss decomposition in large probabilistic graphs.” 2019. Masters Thesis, University of Victoria. Accessed February 19, 2020. http://hdl.handle.net/1828/11428.

MLA Handbook (7^{th} Edition):

Daneshmandmehrabani, Mahsa. “Truss decomposition in large probabilistic graphs.” 2019. Web. 19 Feb 2020.

Vancouver:

Daneshmandmehrabani M. Truss decomposition in large probabilistic graphs. [Internet] [Masters thesis]. University of Victoria; 2019. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/1828/11428.

Council of Science Editors:

Daneshmandmehrabani M. Truss decomposition in large probabilistic graphs. [Masters Thesis]. University of Victoria; 2019. Available from: http://hdl.handle.net/1828/11428

Virginia Tech

14. Cadena, Jose Eduardo. Finding Interesting Subgraphs with Guarantees.

Degree: PhD, Computer Science, 2018, Virginia Tech

URL: http://hdl.handle.net/10919/81960

► Networks are a mathematical abstraction of the interactions between a set of entities, with extensive applications in social science, epidemiology, bioinformatics, and cybersecurity, among others.…
(more)

Subjects/Keywords: Graph Mining; Data Mining; Graph Algorithms; Anomaly Detection; Finding Subgraphs; Parameterized Complexity; Distributed Algorithms

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APA (6^{th} Edition):

Cadena, J. E. (2018). Finding Interesting Subgraphs with Guarantees. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81960

Chicago Manual of Style (16^{th} Edition):

Cadena, Jose Eduardo. “Finding Interesting Subgraphs with Guarantees.” 2018. Doctoral Dissertation, Virginia Tech. Accessed February 19, 2020. http://hdl.handle.net/10919/81960.

MLA Handbook (7^{th} Edition):

Cadena, Jose Eduardo. “Finding Interesting Subgraphs with Guarantees.” 2018. Web. 19 Feb 2020.

Vancouver:

Cadena JE. Finding Interesting Subgraphs with Guarantees. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10919/81960.

Council of Science Editors:

Cadena JE. Finding Interesting Subgraphs with Guarantees. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/81960

Iowa State University

15. Wang, Heyong. A two-stage strategy for solving the connection subgraph problem.

Degree: 2012, Iowa State University

URL: https://lib.dr.iastate.edu/etd/12507

► A connection subgraph is a small subgraph of a large *graph* that best capture the relationship between two nodes. Formally, Connection Subgraph Problem is: Given:…
(more)

Subjects/Keywords: Connection Subgraph; Data Mining; Graph Betweenness; Graph Mining; Path Between; Computer Sciences

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APA (6^{th} Edition):

Wang, H. (2012). A two-stage strategy for solving the connection subgraph problem. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/12507

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Wang, Heyong. “A two-stage strategy for solving the connection subgraph problem.” 2012. Thesis, Iowa State University. Accessed February 19, 2020. https://lib.dr.iastate.edu/etd/12507.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Wang, Heyong. “A two-stage strategy for solving the connection subgraph problem.” 2012. Web. 19 Feb 2020.

Vancouver:

Wang H. A two-stage strategy for solving the connection subgraph problem. [Internet] [Thesis]. Iowa State University; 2012. [cited 2020 Feb 19]. Available from: https://lib.dr.iastate.edu/etd/12507.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang H. A two-stage strategy for solving the connection subgraph problem. [Thesis]. Iowa State University; 2012. Available from: https://lib.dr.iastate.edu/etd/12507

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

16.
Slota, George Michael.
Irregular *Graph* Algorithms on Modern Multicore, Manycore,
and Distributed Processing Systems.

Degree: PhD, Computer Science and Engineering, 2016, Penn State University

URL: https://etda.libraries.psu.edu/catalog/28925

► *Graph* analysis is the study of real-world interaction data, be it through biological or chemical interaction networks, human social or communication networks, or other *graph*-representable…
(more)

Subjects/Keywords: graph mining; high performance computing; graph partitioning; subgraph isomorphism

Record Details Similar Records

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APA (6^{th} Edition):

Slota, G. M. (2016). Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/28925

Chicago Manual of Style (16^{th} Edition):

Slota, George Michael. “Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems.” 2016. Doctoral Dissertation, Penn State University. Accessed February 19, 2020. https://etda.libraries.psu.edu/catalog/28925.

MLA Handbook (7^{th} Edition):

Slota, George Michael. “Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems.” 2016. Web. 19 Feb 2020.

Vancouver:

Slota GM. Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems. [Internet] [Doctoral dissertation]. Penn State University; 2016. [cited 2020 Feb 19]. Available from: https://etda.libraries.psu.edu/catalog/28925.

Council of Science Editors:

Slota GM. Irregular Graph Algorithms on Modern Multicore, Manycore, and Distributed Processing Systems. [Doctoral Dissertation]. Penn State University; 2016. Available from: https://etda.libraries.psu.edu/catalog/28925

Penn State University

17. Chatterjee, Anirban. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.

Degree: PhD, Computer Science and Engineering, 2011, Penn State University

URL: https://etda.libraries.psu.edu/catalog/12026

► Data-driven discovery seeks to obtain a computational model of the underlying process using observed data on a large number of variables. Observations can be viewed…
(more)

Subjects/Keywords: sparse graph embedding; sparse graph partitioning; data mining; sparse linear solvers

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Chatterjee, A. (2011). Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/12026

Chicago Manual of Style (16^{th} Edition):

Chatterjee, Anirban. “Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.” 2011. Doctoral Dissertation, Penn State University. Accessed February 19, 2020. https://etda.libraries.psu.edu/catalog/12026.

MLA Handbook (7^{th} Edition):

Chatterjee, Anirban. “Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.” 2011. Web. 19 Feb 2020.

Vancouver:

Chatterjee A. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. [Internet] [Doctoral dissertation]. Penn State University; 2011. [cited 2020 Feb 19]. Available from: https://etda.libraries.psu.edu/catalog/12026.

Council of Science Editors:

Chatterjee A. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. [Doctoral Dissertation]. Penn State University; 2011. Available from: https://etda.libraries.psu.edu/catalog/12026

Queensland University of Technology

18.
Hassanzadeh, Reza.
Anomaly detection in online social networks : using data-*mining* techniques and fuzzy logic.

Degree: 2014, Queensland University of Technology

URL: https://eprints.qut.edu.au/78679/

► This research is a step forward in improving the accuracy of detecting anomaly in a data *graph* representing connectivity between people in an online social…
(more)

Subjects/Keywords: Anomaly Detection; Fuzzy Logig; Data Mining; Data Graph; Graph Theory

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APA (6^{th} Edition):

Hassanzadeh, R. (2014). Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/78679/

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Hassanzadeh, Reza. “Anomaly detection in online social networks : using data-mining techniques and fuzzy logic.” 2014. Thesis, Queensland University of Technology. Accessed February 19, 2020. https://eprints.qut.edu.au/78679/.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Hassanzadeh, Reza. “Anomaly detection in online social networks : using data-mining techniques and fuzzy logic.” 2014. Web. 19 Feb 2020.

Vancouver:

Hassanzadeh R. Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. [Internet] [Thesis]. Queensland University of Technology; 2014. [cited 2020 Feb 19]. Available from: https://eprints.qut.edu.au/78679/.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hassanzadeh R. Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. [Thesis]. Queensland University of Technology; 2014. Available from: https://eprints.qut.edu.au/78679/

Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

19.
Maxwell, Evan Kyle.
*Graph**Mining* Algorithms for Memory Leak Diagnosis and Biological Database Clustering.

Degree: MS, Computer Science, 2010, Virginia Tech

URL: http://hdl.handle.net/10919/34008

► Large *graph*-based datasets are common to many applications because of the additional structure provided to data by graphs. Patterns extracted from graphs must adhere to…
(more)

Subjects/Keywords: graph mining; graph clustering; multipartite cliques; memory leak detection; bioinformatics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Maxwell, E. K. (2010). Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/34008

Chicago Manual of Style (16^{th} Edition):

Maxwell, Evan Kyle. “Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering.” 2010. Masters Thesis, Virginia Tech. Accessed February 19, 2020. http://hdl.handle.net/10919/34008.

MLA Handbook (7^{th} Edition):

Maxwell, Evan Kyle. “Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering.” 2010. Web. 19 Feb 2020.

Vancouver:

Maxwell EK. Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering. [Internet] [Masters thesis]. Virginia Tech; 2010. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10919/34008.

Council of Science Editors:

Maxwell EK. Graph Mining Algorithms for Memory Leak Diagnosis and Biological Database Clustering. [Masters Thesis]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/34008

University of Melbourne

20. Rashidi, Lida. Anomaly detection in large evolving graphs.

Degree: 2017, University of Melbourne

URL: http://hdl.handle.net/11343/194243

► Anomaly detection plays a vital role in various application domains including network intrusion detection, environmental monitoring and road traffic analysis. However a major challenge in…
(more)

Subjects/Keywords: anomaly detection; graph mining; matrix permutation; graph embedding; dynamic graphs

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APA (6^{th} Edition):

Rashidi, L. (2017). Anomaly detection in large evolving graphs. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/194243

Chicago Manual of Style (16^{th} Edition):

Rashidi, Lida. “Anomaly detection in large evolving graphs.” 2017. Doctoral Dissertation, University of Melbourne. Accessed February 19, 2020. http://hdl.handle.net/11343/194243.

MLA Handbook (7^{th} Edition):

Rashidi, Lida. “Anomaly detection in large evolving graphs.” 2017. Web. 19 Feb 2020.

Vancouver:

Rashidi L. Anomaly detection in large evolving graphs. [Internet] [Doctoral dissertation]. University of Melbourne; 2017. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/11343/194243.

Council of Science Editors:

Rashidi L. Anomaly detection in large evolving graphs. [Doctoral Dissertation]. University of Melbourne; 2017. Available from: http://hdl.handle.net/11343/194243

Arizona State University

21. Peng, Ruiyue. TiCTak: Target-Specific Centrality Manipulation on Large Networks.

Degree: Computer Science, 2016, Arizona State University

URL: http://repository.asu.edu/items/40757

► Measuring node centrality is a critical common denominator behind many important *graph* *mining* tasks. While the existing literature offers a wealth of different node centrality…
(more)

Subjects/Keywords: Computer science; graph connectivity optimization; graph mining; large networks; node centrality

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APA (6^{th} Edition):

Peng, R. (2016). TiCTak: Target-Specific Centrality Manipulation on Large Networks. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/40757

Chicago Manual of Style (16^{th} Edition):

Peng, Ruiyue. “TiCTak: Target-Specific Centrality Manipulation on Large Networks.” 2016. Masters Thesis, Arizona State University. Accessed February 19, 2020. http://repository.asu.edu/items/40757.

MLA Handbook (7^{th} Edition):

Peng, Ruiyue. “TiCTak: Target-Specific Centrality Manipulation on Large Networks.” 2016. Web. 19 Feb 2020.

Vancouver:

Peng R. TiCTak: Target-Specific Centrality Manipulation on Large Networks. [Internet] [Masters thesis]. Arizona State University; 2016. [cited 2020 Feb 19]. Available from: http://repository.asu.edu/items/40757.

Council of Science Editors:

Peng R. TiCTak: Target-Specific Centrality Manipulation on Large Networks. [Masters Thesis]. Arizona State University; 2016. Available from: http://repository.asu.edu/items/40757

Texas A&M University

22.
Sangelkar, Shraddha Chandrakant.
Automated Inclusive Design Heuristics Generation with *Graph* * Mining*.

Degree: 2013, Texas A&M University

URL: http://hdl.handle.net/1969.1/151329

► Inclusive design is a concept intended to promote the development of products and environments equally usable by all users, irrespective of their age or ability.…
(more)

Subjects/Keywords: Inclusive Design; Graph Mining; Data Mining; User Centric Design; Universal Design

Record Details Similar Records

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APA (6^{th} Edition):

Sangelkar, S. C. (2013). Automated Inclusive Design Heuristics Generation with Graph Mining. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/151329

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Sangelkar, Shraddha Chandrakant. “Automated Inclusive Design Heuristics Generation with Graph Mining.” 2013. Thesis, Texas A&M University. Accessed February 19, 2020. http://hdl.handle.net/1969.1/151329.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Sangelkar, Shraddha Chandrakant. “Automated Inclusive Design Heuristics Generation with Graph Mining.” 2013. Web. 19 Feb 2020.

Vancouver:

Sangelkar SC. Automated Inclusive Design Heuristics Generation with Graph Mining. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/1969.1/151329.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sangelkar SC. Automated Inclusive Design Heuristics Generation with Graph Mining. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/151329

Not specified: Masters Thesis or Doctoral Dissertation

Case Western Reserve University

23.
Cederquist, Aaron.
Frequent Pattern *Mining* among Weighted and Directed
Graphs.

Degree: MSs, EECS - Computer and Information Sciences, 2009, Case Western Reserve University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123

► *Mining* frequent *graph* patterns has great practical implications, since data in numerous application domains such as biology, sociology, and finance, can be represented as graphs.…
(more)

Subjects/Keywords: Computer Science; data mining; graph; canonical form; frequent pattern mining

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Cederquist, A. (2009). Frequent Pattern Mining among Weighted and Directed Graphs. (Masters Thesis). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123

Chicago Manual of Style (16^{th} Edition):

Cederquist, Aaron. “Frequent Pattern Mining among Weighted and Directed Graphs.” 2009. Masters Thesis, Case Western Reserve University. Accessed February 19, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123.

MLA Handbook (7^{th} Edition):

Cederquist, Aaron. “Frequent Pattern Mining among Weighted and Directed Graphs.” 2009. Web. 19 Feb 2020.

Vancouver:

Cederquist A. Frequent Pattern Mining among Weighted and Directed Graphs. [Internet] [Masters thesis]. Case Western Reserve University; 2009. [cited 2020 Feb 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123.

Council of Science Editors:

Cederquist A. Frequent Pattern Mining among Weighted and Directed Graphs. [Masters Thesis]. Case Western Reserve University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123

University of Oregon

24.
Liu, Haishan.
A *Graph*-based Approach for Semantic Data * Mining*.

Degree: 2012, University of Oregon

URL: http://hdl.handle.net/1794/12567

► Data *mining* is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is widely acknowledged that the role of domain…
(more)

Subjects/Keywords: domain knowledge; graph mining; ontology; semantic data mining

Record Details Similar Records

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APA (6^{th} Edition):

Liu, H. (2012). A Graph-based Approach for Semantic Data Mining. (Thesis). University of Oregon. Retrieved from http://hdl.handle.net/1794/12567

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Liu, Haishan. “A Graph-based Approach for Semantic Data Mining.” 2012. Thesis, University of Oregon. Accessed February 19, 2020. http://hdl.handle.net/1794/12567.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Liu, Haishan. “A Graph-based Approach for Semantic Data Mining.” 2012. Web. 19 Feb 2020.

Vancouver:

Liu H. A Graph-based Approach for Semantic Data Mining. [Internet] [Thesis]. University of Oregon; 2012. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/1794/12567.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu H. A Graph-based Approach for Semantic Data Mining. [Thesis]. University of Oregon; 2012. Available from: http://hdl.handle.net/1794/12567

Not specified: Masters Thesis or Doctoral Dissertation

25.
Voudigari, Elli.
*Graph**mining* In complex networks and prediction in the web * graph*.

Degree: 2018, Athens University Economics and Business (AUEB); Οικονομικό Πανεπιστήμιο Αθηνών

URL: http://hdl.handle.net/10442/hedi/44611

► The study of real world networks has attracted considerable attention over the last decades from many disciplines including biology, computer science, economics, engineering, mathematics, physics,…
(more)

Subjects/Keywords: Εξόρυξη γνώσης από γράφους; Graph mining; Web mining

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APA (6^{th} Edition):

Voudigari, E. (2018). Graph mining In complex networks and prediction in the web graph. (Thesis). Athens University Economics and Business (AUEB); Οικονομικό Πανεπιστήμιο Αθηνών. Retrieved from http://hdl.handle.net/10442/hedi/44611

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Voudigari, Elli. “Graph mining In complex networks and prediction in the web graph.” 2018. Thesis, Athens University Economics and Business (AUEB); Οικονομικό Πανεπιστήμιο Αθηνών. Accessed February 19, 2020. http://hdl.handle.net/10442/hedi/44611.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Voudigari, Elli. “Graph mining In complex networks and prediction in the web graph.” 2018. Web. 19 Feb 2020.

Vancouver:

Voudigari E. Graph mining In complex networks and prediction in the web graph. [Internet] [Thesis]. Athens University Economics and Business (AUEB); Οικονομικό Πανεπιστήμιο Αθηνών; 2018. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/10442/hedi/44611.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Voudigari E. Graph mining In complex networks and prediction in the web graph. [Thesis]. Athens University Economics and Business (AUEB); Οικονομικό Πανεπιστήμιο Αθηνών; 2018. Available from: http://hdl.handle.net/10442/hedi/44611

Not specified: Masters Thesis or Doctoral Dissertation

Université Catholique de Louvain

26. Senelle, Mathieu. Measures on graphs : from similarity to density.

Degree: 2014, Université Catholique de Louvain

URL: http://hdl.handle.net/2078.1/161671

►

In this thesis, we mainly consider two closely related classical and general problems in data *mining* and machine learning: (1) developing new similarity measures between…
(more)

Subjects/Keywords: Data Mining; Graph Mining; Classification; Clustering; Matrix-Forest Theorem

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APA (6^{th} Edition):

Senelle, M. (2014). Measures on graphs : from similarity to density. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/161671

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Senelle, Mathieu. “Measures on graphs : from similarity to density.” 2014. Thesis, Université Catholique de Louvain. Accessed February 19, 2020. http://hdl.handle.net/2078.1/161671.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Senelle, Mathieu. “Measures on graphs : from similarity to density.” 2014. Web. 19 Feb 2020.

Vancouver:

Senelle M. Measures on graphs : from similarity to density. [Internet] [Thesis]. Université Catholique de Louvain; 2014. [cited 2020 Feb 19]. Available from: http://hdl.handle.net/2078.1/161671.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Senelle M. Measures on graphs : from similarity to density. [Thesis]. Université Catholique de Louvain; 2014. Available from: http://hdl.handle.net/2078.1/161671

Not specified: Masters Thesis or Doctoral Dissertation

Purdue University

27.
Ahmed, Nesreen Kamel.
Scaling Up Network Analysis and *Mining*: Statistical Sampling, Estimation, and Pattern Discovery.

Degree: PhD, Computer Science, 2015, Purdue University

URL: https://docs.lib.purdue.edu/open_access_dissertations/1445

► Network analysis and *graph* *mining* play a prominent role in providing insights and studying phenomena across various domains, including social, behavioral, biological, transportation, communication, and…
(more)

Subjects/Keywords: Data Mining; Graph Mining; Machine Learning; Network Science; Sampling; Statistical Estimation

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Ahmed, N. K. (2015). Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/1445

Chicago Manual of Style (16^{th} Edition):

Ahmed, Nesreen Kamel. “Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery.” 2015. Doctoral Dissertation, Purdue University. Accessed February 19, 2020. https://docs.lib.purdue.edu/open_access_dissertations/1445.

MLA Handbook (7^{th} Edition):

Ahmed, Nesreen Kamel. “Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery.” 2015. Web. 19 Feb 2020.

Vancouver:

Ahmed NK. Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery. [Internet] [Doctoral dissertation]. Purdue University; 2015. [cited 2020 Feb 19]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1445.

Council of Science Editors:

Ahmed NK. Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery. [Doctoral Dissertation]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1445

Iowa State University

28.
Mousavi Hanjani, Kiana.
Improved triangle counting in *graph* streams: Neighborhood multi-sampling.

Degree: 2018, Iowa State University

URL: https://lib.dr.iastate.edu/etd/16644

► In this thesis, we study the problem of estimating the number of triangles of an undirected *graph* in the data stream model. Some of the…
(more)

Subjects/Keywords: big data; data mining; graph mining; streaming algorithms; Computer Sciences

Record Details Similar Records

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APA (6^{th} Edition):

Mousavi Hanjani, K. (2018). Improved triangle counting in graph streams: Neighborhood multi-sampling. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16644

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Mousavi Hanjani, Kiana. “Improved triangle counting in graph streams: Neighborhood multi-sampling.” 2018. Thesis, Iowa State University. Accessed February 19, 2020. https://lib.dr.iastate.edu/etd/16644.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Mousavi Hanjani, Kiana. “Improved triangle counting in graph streams: Neighborhood multi-sampling.” 2018. Web. 19 Feb 2020.

Vancouver:

Mousavi Hanjani K. Improved triangle counting in graph streams: Neighborhood multi-sampling. [Internet] [Thesis]. Iowa State University; 2018. [cited 2020 Feb 19]. Available from: https://lib.dr.iastate.edu/etd/16644.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mousavi Hanjani K. Improved triangle counting in graph streams: Neighborhood multi-sampling. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/16644

Not specified: Masters Thesis or Doctoral Dissertation

Kent State University

29.
Liu, Yufan.
A Survey Of Persistent *Graph* Databases.

Degree: MS, College of Arts and Sciences / Department of Computer Science, 2014, Kent State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=kent1395166105

► *Graph* database has attracted increasing attention from both of the database and data *mining*/machine learning communities. Enormous kinds of data with complex and dynamic relationships…
(more)

Subjects/Keywords: Computer Engineering; Computer Science; graph database; graph mining; transactional database; benchmark; graph algorithm; GDB; distribute graph processing framework; NOSQL

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Liu, Y. (2014). A Survey Of Persistent Graph Databases. (Masters Thesis). Kent State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=kent1395166105

Chicago Manual of Style (16^{th} Edition):

Liu, Yufan. “A Survey Of Persistent Graph Databases.” 2014. Masters Thesis, Kent State University. Accessed February 19, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=kent1395166105.

MLA Handbook (7^{th} Edition):

Liu, Yufan. “A Survey Of Persistent Graph Databases.” 2014. Web. 19 Feb 2020.

Vancouver:

Liu Y. A Survey Of Persistent Graph Databases. [Internet] [Masters thesis]. Kent State University; 2014. [cited 2020 Feb 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=kent1395166105.

Council of Science Editors:

Liu Y. A Survey Of Persistent Graph Databases. [Masters Thesis]. Kent State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=kent1395166105

Carnegie Mellon University

30.
Kang, U.
* Mining* Tera-Scale Graphs: Theory, Engineering and Discoveries.

Degree: 2012, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/160

► How do we find patterns and anomalies, on graphs with billions of nodes and edges, which do not fit in memory? How to use parallelism…
(more)

Subjects/Keywords: graph mining; MAPREDUCE; HADOOP; graph structure analysis; radius plot; diameter; connected component; inference; spectral graph analysis; eigensolver; tensor analysis; graph management; graph indexing; graph compression; Computer Sciences

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Kang, U. (2012). Mining Tera-Scale Graphs: Theory, Engineering and Discoveries. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/160

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Kang, U. “Mining Tera-Scale Graphs: Theory, Engineering and Discoveries.” 2012. Thesis, Carnegie Mellon University. Accessed February 19, 2020. http://repository.cmu.edu/dissertations/160.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Kang, U. “Mining Tera-Scale Graphs: Theory, Engineering and Discoveries.” 2012. Web. 19 Feb 2020.

Vancouver:

Kang U. Mining Tera-Scale Graphs: Theory, Engineering and Discoveries. [Internet] [Thesis]. Carnegie Mellon University; 2012. [cited 2020 Feb 19]. Available from: http://repository.cmu.edu/dissertations/160.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kang U. Mining Tera-Scale Graphs: Theory, Engineering and Discoveries. [Thesis]. Carnegie Mellon University; 2012. Available from: http://repository.cmu.edu/dissertations/160

Not specified: Masters Thesis or Doctoral Dissertation