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You searched for subject:(community structure detection). Showing records 1 – 9 of 9 total matches.

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Purdue University

1. Bang, Seokhun. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.

Degree: MSIE, Industrial Engineering, 2015, Purdue University

 Graph clustering is a field of study that helps reveal characteristics of communities. Systems can be viewed as networks and form communities in various areas… (more)

Subjects/Keywords: Clustering algorithm; Community detection; Community structure; Label Propagation Method; LabelRank; Markov Cluster Algorithm

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

APA (6th Edition):

Bang, S. (2015). Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. (Thesis). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_theses/1046

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):

Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Thesis, Purdue University. Accessed January 25, 2020. https://docs.lib.purdue.edu/open_access_theses/1046.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Web. 25 Jan 2020.

Vancouver:

Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Internet] [Thesis]. Purdue University; 2015. [cited 2020 Jan 25]. Available from: https://docs.lib.purdue.edu/open_access_theses/1046.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Thesis]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_theses/1046

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Purdue University

2. Bang, Seokhun. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.

Degree: MSIE, Industrial Engineering, 2015, Purdue University

  Graph clustering is a field of study that helps reveal characteristics of communities. Systems can be viewed as networks and form communities in various… (more)

Subjects/Keywords: Clustering algorithm; Community detection; Community structure; Label Propagation Method; LabelRank; Markov Cluster Algorithm

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

APA (6th Edition):

Bang, S. (2015). Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. (Thesis). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_theses/1037

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):

Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Thesis, Purdue University. Accessed January 25, 2020. https://docs.lib.purdue.edu/open_access_theses/1037.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bang, Seokhun. “Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs.” 2015. Web. 25 Jan 2020.

Vancouver:

Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Internet] [Thesis]. Purdue University; 2015. [cited 2020 Jan 25]. Available from: https://docs.lib.purdue.edu/open_access_theses/1037.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bang S. Community Detection Using Efficient Modularity Optimization Method: LabelMod with Single and Multi-Layer Graphs. [Thesis]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_theses/1037

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Australian National University

3. Rezvani, Mojtaba. Community Structure in Large-Scale Complex Networks .

Degree: 2019, Australian National University

 Vertices in complex networks can be grouped into communities, where vertices inside communities are densely connected to each other and vertices from one community are… (more)

Subjects/Keywords: complex networks; community structure; community detection; community search; overlapping community detection; structural hole spanners; social networks; large-scale networks; large-scale graphs

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APA (6th Edition):

Rezvani, M. (2019). Community Structure in Large-Scale Complex Networks . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/187032

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):

Rezvani, Mojtaba. “Community Structure in Large-Scale Complex Networks .” 2019. Thesis, Australian National University. Accessed January 25, 2020. http://hdl.handle.net/1885/187032.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rezvani, Mojtaba. “Community Structure in Large-Scale Complex Networks .” 2019. Web. 25 Jan 2020.

Vancouver:

Rezvani M. Community Structure in Large-Scale Complex Networks . [Internet] [Thesis]. Australian National University; 2019. [cited 2020 Jan 25]. Available from: http://hdl.handle.net/1885/187032.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rezvani M. Community Structure in Large-Scale Complex Networks . [Thesis]. Australian National University; 2019. Available from: http://hdl.handle.net/1885/187032

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

4. Wang, Qinna. Détection de communautés recouvrantes dans des réseaux de terrain dynamiques : Overlapping community detection in dynamic networks.

Degree: Docteur es, Informatique, 2012, Lyon, École normale supérieure

Dans le contexte des réseaux complexes, la structure communautaire du réseau devient un sujet important pour plusieurs domaines de recherche. Les communautés sont en général… (more)

Subjects/Keywords: Structure communautaire; Communautés recouvrantes; Réseaux de terrain dynamiques; Evolution de communautés; Réseaux complexes; Community detection; Overlapping community structure; Complex networks; Dynamic networks; Community evolution; Network science

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

APA (6th Edition):

Wang, Q. (2012). Détection de communautés recouvrantes dans des réseaux de terrain dynamiques : Overlapping community detection in dynamic networks. (Doctoral Dissertation). Lyon, École normale supérieure. Retrieved from http://www.theses.fr/2012ENSL0713

Chicago Manual of Style (16th Edition):

Wang, Qinna. “Détection de communautés recouvrantes dans des réseaux de terrain dynamiques : Overlapping community detection in dynamic networks.” 2012. Doctoral Dissertation, Lyon, École normale supérieure. Accessed January 25, 2020. http://www.theses.fr/2012ENSL0713.

MLA Handbook (7th Edition):

Wang, Qinna. “Détection de communautés recouvrantes dans des réseaux de terrain dynamiques : Overlapping community detection in dynamic networks.” 2012. Web. 25 Jan 2020.

Vancouver:

Wang Q. Détection de communautés recouvrantes dans des réseaux de terrain dynamiques : Overlapping community detection in dynamic networks. [Internet] [Doctoral dissertation]. Lyon, École normale supérieure; 2012. [cited 2020 Jan 25]. Available from: http://www.theses.fr/2012ENSL0713.

Council of Science Editors:

Wang Q. Détection de communautés recouvrantes dans des réseaux de terrain dynamiques : Overlapping community detection in dynamic networks. [Doctoral Dissertation]. Lyon, École normale supérieure; 2012. Available from: http://www.theses.fr/2012ENSL0713

5. Orman, Keziban. Contribution to the interpretation of evolving communities in complex networks : Application to the study of social interactions : Contribution à l’interprétation des communautés en évolution dans des réseaux complexes : Application à l’étude des interactions sociales.

Degree: Docteur es, Informatique, 2014, INSA Lyon

Les réseaux complexes constituent un outil pratique pour modéliser les systèmes complexes réels. Pour cette raison, ils sont devenus très populaires au cours de la… (more)

Subjects/Keywords: Informatique; Réseaux complexes; Réseaux dynamiques attribués; Structure communautaire; Inrerprétation des communautés; Détection des anomalies; Information Technology; Complex Networks; Dynamic attributed Network; Community structure; Community interpretation; Anomaly detection; 006.330 72

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

APA (6th Edition):

Orman, K. (2014). Contribution to the interpretation of evolving communities in complex networks : Application to the study of social interactions : Contribution à l’interprétation des communautés en évolution dans des réseaux complexes : Application à l’étude des interactions sociales. (Doctoral Dissertation). INSA Lyon. Retrieved from http://www.theses.fr/2014ISAL0072

Chicago Manual of Style (16th Edition):

Orman, Keziban. “Contribution to the interpretation of evolving communities in complex networks : Application to the study of social interactions : Contribution à l’interprétation des communautés en évolution dans des réseaux complexes : Application à l’étude des interactions sociales.” 2014. Doctoral Dissertation, INSA Lyon. Accessed January 25, 2020. http://www.theses.fr/2014ISAL0072.

MLA Handbook (7th Edition):

Orman, Keziban. “Contribution to the interpretation of evolving communities in complex networks : Application to the study of social interactions : Contribution à l’interprétation des communautés en évolution dans des réseaux complexes : Application à l’étude des interactions sociales.” 2014. Web. 25 Jan 2020.

Vancouver:

Orman K. Contribution to the interpretation of evolving communities in complex networks : Application to the study of social interactions : Contribution à l’interprétation des communautés en évolution dans des réseaux complexes : Application à l’étude des interactions sociales. [Internet] [Doctoral dissertation]. INSA Lyon; 2014. [cited 2020 Jan 25]. Available from: http://www.theses.fr/2014ISAL0072.

Council of Science Editors:

Orman K. Contribution to the interpretation of evolving communities in complex networks : Application to the study of social interactions : Contribution à l’interprétation des communautés en évolution dans des réseaux complexes : Application à l’étude des interactions sociales. [Doctoral Dissertation]. INSA Lyon; 2014. Available from: http://www.theses.fr/2014ISAL0072


Linnaeus University

6. Maryokhin, Tymur. Data dissemination in large-cardinality social graphs.

Degree: Computer Science, 2015, Linnaeus University

  Near real-time event streams are a key feature in many popular social media applications. These types of applications allow users to selectively follow event… (more)

Subjects/Keywords: Data dissemination; message delivery; social graph; big data; large scale; feed following; materialized views; social network analysis; community structure detection; graph theory; database theory.; Computer Systems; Datorsystem

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APA (6th Edition):

Maryokhin, T. (2015). Data dissemination in large-cardinality social graphs. (Thesis). Linnaeus University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-48268

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):

Maryokhin, Tymur. “Data dissemination in large-cardinality social graphs.” 2015. Thesis, Linnaeus University. Accessed January 25, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-48268.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Maryokhin, Tymur. “Data dissemination in large-cardinality social graphs.” 2015. Web. 25 Jan 2020.

Vancouver:

Maryokhin T. Data dissemination in large-cardinality social graphs. [Internet] [Thesis]. Linnaeus University; 2015. [cited 2020 Jan 25]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-48268.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Maryokhin T. Data dissemination in large-cardinality social graphs. [Thesis]. Linnaeus University; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-48268

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Florida

7. Nguyen, Nam P. Community Structure and Its Applications in Dynamic Complex Networks.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2013, University of Florida

 In this dissertation, we focus on analyzing and understanding the organizational principals, assessing the structural vulnerability as well as exploring practical applications of dynamic complex… (more)

Subjects/Keywords: Community structure; Datasets; Discourse communities; Modularity; Online communities; Online social networking; Social media; Social networking; Uniform Resource Locators; Worms; adaptive-algorithm  – community-detection  – dynamic-complex-network  – fowarding-routing-stategy  – mobile-network  – online-social-network  – stable-community  – worm-containment

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

APA (6th Edition):

Nguyen, N. P. (2013). Community Structure and Its Applications in Dynamic Complex Networks. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0045408

Chicago Manual of Style (16th Edition):

Nguyen, Nam P. “Community Structure and Its Applications in Dynamic Complex Networks.” 2013. Doctoral Dissertation, University of Florida. Accessed January 25, 2020. http://ufdc.ufl.edu/UFE0045408.

MLA Handbook (7th Edition):

Nguyen, Nam P. “Community Structure and Its Applications in Dynamic Complex Networks.” 2013. Web. 25 Jan 2020.

Vancouver:

Nguyen NP. Community Structure and Its Applications in Dynamic Complex Networks. [Internet] [Doctoral dissertation]. University of Florida; 2013. [cited 2020 Jan 25]. Available from: http://ufdc.ufl.edu/UFE0045408.

Council of Science Editors:

Nguyen NP. Community Structure and Its Applications in Dynamic Complex Networks. [Doctoral Dissertation]. University of Florida; 2013. Available from: http://ufdc.ufl.edu/UFE0045408

8. Khaniyev, Taghi. Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity.

Degree: 2018, University of Waterloo

 Employing data-driven methods to efficiently solve practical and large optimization problems is a recent trend that focuses on identifying patterns and structures in the problem… (more)

Subjects/Keywords: structure detection; bordered block diagonal; Dantzig-Wolfe decomposition; community detection; hub location; brain connectivity networks

…establishes the connection between community detection and BBD structure detection. Definition 3… …detection problem seeks to find the community structure = {V1 , V2 , ..., VK } and the… …community detection has not been investigated in the context of BBD structure detection. Assessing… …contribution of this work is a new BBD structure detection approach based on community detection that… …we introduce a BBD structure detection approach based on community detection and… 

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

APA (6th Edition):

Khaniyev, T. (2018). Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13476

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):

Khaniyev, Taghi. “Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity.” 2018. Thesis, University of Waterloo. Accessed January 25, 2020. http://hdl.handle.net/10012/13476.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Khaniyev, Taghi. “Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity.” 2018. Web. 25 Jan 2020.

Vancouver:

Khaniyev T. Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2020 Jan 25]. Available from: http://hdl.handle.net/10012/13476.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khaniyev T. Data-driven Structure Detection in Optimization: Decomposition, Hub Location, and Brain Connectivity. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13476

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

9. Zhou, Bo. Novel Bayesian Methods in Neuroscience.

Degree: Statistics, 2015, University of California – Irvine

 For an individual to successfully complete the task of decision-making, a set of temporally-organized events must occur: stimuli must be detected,potential outcomes must be evaluated,… (more)

Subjects/Keywords: Statistics; Bayesian Copula Model; Community Detection; Dynamic Dependence Structure; Spike Train

community structure of correlated neurons. Each node represents one neuron and each edge… …Time-varying community structure of correlated neurons. For the first half of time, there are… …neuron 5 joins the second community in stead of neuron 6. The community structure of correlated… …of the covariates, x∗j . Our model for community detection among spike trains can be… …blockmodels which shows substantial improvement for community detection in complex networks while… 

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APA (6th Edition):

Zhou, B. (2015). Novel Bayesian Methods in Neuroscience. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/0k73s50v

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):

Zhou, Bo. “Novel Bayesian Methods in Neuroscience.” 2015. Thesis, University of California – Irvine. Accessed January 25, 2020. http://www.escholarship.org/uc/item/0k73s50v.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhou, Bo. “Novel Bayesian Methods in Neuroscience.” 2015. Web. 25 Jan 2020.

Vancouver:

Zhou B. Novel Bayesian Methods in Neuroscience. [Internet] [Thesis]. University of California – Irvine; 2015. [cited 2020 Jan 25]. Available from: http://www.escholarship.org/uc/item/0k73s50v.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhou B. Novel Bayesian Methods in Neuroscience. [Thesis]. University of California – Irvine; 2015. Available from: http://www.escholarship.org/uc/item/0k73s50v

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.