algorithms for community detection in large networks (and guidelines on cs3230r)

Hon Wai Leong, NUS(… Finding Communities)

© Leong Hon Wai, 2013

Leong Hon Wai (梁汉槐 )Department of Computer ScienceNational University of Singapore

[email protected]://www.comp.nus.edu.sg/~leonghw/

CS3230R Talk: 13 Feb 2014

Algorithms for Community Detection

in Large Networks(And guidelines on CS3230R)

Hon Wai Leong, Computer Science, NUS(PPI Complex Detection, Sep 2013)

© Leong Hon Wai

For CS3230R

Choose CD algorithm(s) Check availability of code READ and understand chosen algorithm Quick survey CLOSELY-related algorithms Prepare implementation, test, evaluation Prepare report Prepare presentation


© Leong Hon Wai

CS3230 Talks

Need Talk on Testing of CD Algorithms Schedule

20-Feb (Wk 6) – Disc. and Choosing Topics 27-Feb (Break) – no talk 06-Mar (Wk 7) – Feedback, Plan 13-Mar (Wk 8) – Davin, WenBo 20-Mar (Wk 9) – Yujian, Darius 27-Mar (Wk 10) – ?? 03-Apr (Wk 11) – ??


© Leong Hon Wai

Large Real-World Networks

Internet graphs, WWW graphs Citation networks, actor networks Transportation network, Email networks Food Web, Social Networks (FB, Linked-In, etc) Biochemical networks Protein-Protein Interaction (PPI) networks


© Leong Hon Wai

Community Structure (example)


© Leong Hon Wai

Community Structure

“groups of vertices with dense intra-group connections, and sparse inter-group connections.”

Within-group (intra-group) edges. High density

Between-group (inter-group) edges. Low density.


© Leong Hon Wai

Examples of Community Structures

Communities of biochemical network might correspond to “functional units” of some kind.

Communities of a web graph might correspond to sets of “web sites dealing with a related topics”.


© Leong Hon Wai

Community Structure (example)


© Leong Hon Wai

Where is the Rabbit (Sept 2013)

Typhoon Usagi ( ウサギ , rabbit) (16-24 Sept 2013)

http://en.wikipedia.org/wiki/Typhoon_Usagi_(2013)


© Leong Hon Wai

Outline of Talk

Large Networks are Everywhere Community Detection: A Quick Overview

Application in Computational Biology

Protein Complex Detection Specialized Algorithms Performance Evaluation Challenges and Conclusion

http://www.cscs.umich.edu/~crshalizi/notebooks/community-discovery.html

* Recommended: Aaron Clauset, "Finding local community structure in networks", physics/0503036 = Physical Review E 72 (2005): 026132 [Clever; but then, Aaron is clever.] * Aaron Clauset, M. E. J. Newman and Cristopher Moore, "Finding Community Structure in Very Large Networks", cond-mat/0408187 = Physical Review E 70 (2004): 066111 * J.-J. Daudin, F. Picard and S. Robin, "A Mixture Model for Random Graphs", Statistics and Computing 18 (2008): 173--183 * Michelle Girvan and M. E. J. Newman, "Community structure in social and biological networks," cond-mat/0112110 = Proceedings of the National Academy of Sciences (USA) 99 (2002): 7821--7826 * Roger Guimera, Marta Sales-Pardo and Luis A. N. Amaral, "Modularity from Fluctuations in Random Graphs", cond-mat/0403660 = Physical Review E 70 (2004): 025101 * Jake M. Hofman, Chris H. Wiggins, "A Bayesian Approach to Network Modularity", arxiv:0709.3512 [For "Bayesian", read "smoothed maximum likelihood". But nonetheless: cool.] * Andrea Lancichinetti, Santo Fortunato, Janos Kertesz, "Detecting the overlapping and hierarchical community structure of complex networks", arxiv:0802.1218 [An interesting approach, but not quite as novel as they claim --- cf. Reichardt and Bornholdt --- and I'd really like to see more evidence of superior accuracy and/or robustness] * E. A. Leicht, M. E. J. Newman, "Community structure in directed networks", arxiv:0709.4500 * M. E. J. Newman o "Modularity and community structure in networks", physics/0602124 = Proceedings of the National Academy of Sciences (USA) 103 (2006): 87577--8582 o "Finding community structure in networks using the eigenvectors of matrices", Physical Review E 74 (2006): 036104 = physics/0605087 * M. E. J. Newman and Michelle Girvan o "Mixing patterns and community structure in networks", cond-mat/0210146 o "Finding and evaluating community structure in networks", Physical Review E 69 (2003): 026113 = cond-mat/0308217 * Jörg Reichardt and Stefan Bornholdt [Code is available by e-mail from Reichardt, who was very helpful to me when I needed to implement their algorithm.] o "Detecting Fuzzy Community Structures in Complex Networks with a Potts Model", Physical Review Letters 93 (2004): 218701 = cond-mat/0402349 o "Statistical Mechanics of Community Detection", cond-mat/0603718 = Physical Review E 74 (2006): 016110 o "Clustering of sparse data via network communities — a prototype study of a large online market", Journal of Statistical Mechanics: Theory and Experiment (2007): P06016 * Jörg Reichardt and Douglas R. White, "Role models for complex networks", arxiv:0708.0958 [Discussion] * M. Sales-Pardo, R. Guimera, A. Moreira, L. Amaral, "Extracting the hierarchical organization of complex systems", arxiv:0705.1679

* Modesty forbids me to recommend: CRS, Marcelo F. Camperi and Kristina Lisa Klinkner, "Discovering Functional Communities in Dynamical Networks", q-bio.NC/0609008

* To read: Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg and Eric P. Xing, "Mixed membership stochastic blockmodels", arxiv:0705.4485 * Nelson Augusto Alves, "Unveiling community structures in weighted networks", physics/0703087 * Leonardo Angelini, Stefano Boccaletti, Daniele Marinazzo, Mario Pellicoro, and Sebastiano Stramaglia, "Fast identification of network modules by optimization of ratio association", cond-mat/0610182 * L. Angelini, D. Marinazzo, M. Pellicoro and S. Stramaglia, "Natural clustering: the modularity approach", cond-mat/0607643 * A. Arenas, J. Duch, A. Fernandez, S. Gomez, "Size reduction of complex networks preserving modularity", physics/0702015 [Do you really need all those links? Wouldn't your life be simpler if you could just ignore some of them?] * Alex Arenas, Alberto Fernandez, Sergio Gomez, "Multiple resolution of the modular structure of complex networks", physics/0703218 * Alex Arenas, Alberto Fernandez, Santo Fortunato, Sergio Gomez, "Motif-based communities in complex networks", arxiv:0710.0059 * Jim Bagrow and Erik Bollt, "A Local Method for Detecting Communities", cond-mat/0412482 * James Bagrow, Erik Bollt, Luciano da F. Costa, "Network Structure Revealed by Short Cycles", cond-mat/0612502 * S. Boccaletti, M. Ivanchenko, V. Latora, A. Pluchino and A. Rapisarda, "Dynamical clustering methods to find community structures", physics/0607179 * Michael James Bommarito II, Daniel Martin Katz, Jon Zelner, "On the Stability of Community Detection Algorithms on Longitudinal Citation Data", arxiv:0908.0449 * U. Brandes, D. Delling, M. Gaertler, R. Goerke, M. Hoefer, Z. Nikoloski, and D. Wagner, "Maximizing Modularity is hard", physics/0608255 [i.e., maximizing Newman's Q is NP hard. I haven't read beyond the abstract yet, so I don't know if they address the question of what makes it hard in the hard cases, and whether those are properties we should expect to see in real-world networks. Conceivably, actual social networks are, on average, easy to modularize...] * Andrea Capocci, Vito D. P. Servedio, Guido Caldarelli, Francesca Colaiori, "Detecting communities in large networks", cond-mat/0402499 * Horacio Castellini and Lilia Romanelli, "Social network from communities of electronic mail", nlin.CD/0509021 * Leon Danon, Albert Díaz-Guilera, and Alex Arenas, "The effect of size heterogeneity on community identification in complex networks", Journal of Statistical Mechanics: Theory and Experiment (2006): P11010 = physics/0601144 * Leon Danon, Albert Díaz-Guilera, Jordi Duch and Alex Arenas, "Comparing community structure identification", Journal of Statistical Mechanics: Theory and Experiment (2005): P09008 = cond-mat/0505245 * Jordi Duch and Alex Arenas, "Community detection in complex networks using extremal optimization", Physical Review E 72 (2005): 027104 * Illes J. Farkas, Daniel Abel, Gergely Palla, Tamas Vicsek, "Weighted network modules", cond-mat/0703706 * Sam Field, Kenneth A. Frank, Kathryn Schiller, Catherine Riegle-Crumb and Chandra Muller, "Identifying positions from affiliation networks: Preserving the duality of people and events", Social Networks 28 (2006): 97--123 * G. W. Flake, S. R. Lawrence, C. L. Giles and F. M. Coetzee, "Self-organization and identification of Web communities", IEEE Computer 36 (2002): 66--71 * Santo Fortunato, "Community detection in graphs", arxiv:0906.0612 * Santo Fortunato and Marc Bathélemy, "Resolution limit in community detection", physics/0607100 = cite>Proceedings of the National Academy of Sciences (USA) 104 (2007): 36--41 * Santo Fortunato and Claudio Castellano, "Community Structure in Graphs", arxiv:0712.2716 [Review paper; thanks to Ed Vielmetti for the pointer] * Santo Fortunato, Vito Latora and Massimo Marchiori, "A Method to Find Community Structures Based on Information Centrality", cond-mat/0402522 * Kenneth A. Frank, "Identifying Cohesive Subgroups", Social Networks 17 (1995): 27--56 * David Gfeller, Jean-Cédric Chappelier, and Paolo De Los Rios, "Finding instabilities in the community structure of complex networks", Physical Review E 72 (2005): 056135 * Rumi Ghosh, Kristina Lerman, "Structure of Heterogeneous Networks", arxiv:0906.2212 * V. Gol'dshtein and G. A. Koganov, "An indicator for community structure", physics/0607159 * Mark S. Handcock, Adrian E. Raftery and Jeremy Tantrum, "Model-Based Clustering for Social Networks" Journal of the Royal Statistical Society A 170 (2007): 301--354 [PDF preprint] * M. B. Hastings, "Community detection as an inference problem", Physical Review E 74 (2006): 035102 = cond-mat/0604429 * Erik Holmström, Nicolas Bock and Joan Brännlund, "Density Analysis of Network Community Divisions", cond-mat/0608612 * I. Ispolatov, I. Mazo, A. Yuryev, "Finding mesoscopic communities in sparse networks", q-bio.MN/0512038 = Journal of Statistical Mechanics (2006): P09014 * Brian Karrer, Elizaveta Levina, M. E. J. Newman, "Robustness of community structure in networks", arxiv:0709.2108 * Jussi M. Kumpula, Jari Saramaki, Kimmo Kaski, and Janos Kertesz, "Resolution limit in complex network community detection with Potts model approach",cond-mat/0610370 * Andrea Lancichinetti, Santo Fortunato, "Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities", arxiv:0904.3940 * Sune Lehmann, Martin Schwartz, Lars Kai Hansen, "Bi-clique Communities", arxiv:0710.4867 * Michele Leone, Sumedha, Martin Weigt, "Clustering by soft-constraint affinity propagation: Applications to gene-expression data", arxiv:0705.2646 * Claire P. Massen, Jonathan P. K. Doye, "Thermodynamics of Community Structure", cond-mat/0610077 * Ian X.Y. Leung, Pan Hui, Pietro Lio', Jon Crowcroft, "Towards Real Time Community Detection in Large Networks", arxiv:0808.2633 * Stefanie Muff, Francesco Rao, and Amedeo Caflisch, "Local modularity measure for network clusterizations", Physical Review E 72 (2005): 056107 * Andreas Noack, "Modularity clustering is force-directed layout", arxiv:0807.4052 * Gergely Palla, Imre Derenyi, Illes Farkas and Tamas Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society", Nature 435 (2005): 814--818 = physics/0506133 * Gergely Palla, Illes J. Farkas, Peter Pollner, Imre Derenyi, Tamas Vicsek, "Directed network modules", physics/0703248 * Nicolas Pissard and Houssem Assadi, "Detecting overlapping communities in linear time with P&A algorithm", physics/0509254 * Pascal Pons, "Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View", cs.DS/0608050 * Mason A. Porter, Jukka-Pekka Onnela, Peter J. Mucha, "Communities in Networks", arxiv:0902.3788 * Josep M. Pujol, Javier Béjar, and Jordi Delgado, "Clustering algorithm for determining community structure in large networks", Physical Review E 74 (2006): 016107 * Francisco A. Rodrigues, Gonzalo Travieso, Luciano da F. Costa, "Fast Community Identification by Hierarchical Growth", physics/0602144 * Huaijun Qiu and Edwin R. Hancock, "Graph matching and clustering using spectral partitions", Pattern Recognition 39 (2006): 22--34 [In this context, for the ideas on hierarchical decomposition, which sounds like it might work for community discovery, if in fact it's not equivalent to some existing community-discovery algorithm.] * Usha Nandini Raghavan, Reka Albert, Soundar Kumara, "Near linear time algorithm to detect community structures in large-scale networks", arxiv:0709.2938 ["every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have"] * Jörg Reichardt and Stefan Bornholdt, "When are networks truly modular?", cond-mat/0606220 * Jörg Reichardt and Michele Leone, "(Un)detectable cluster structure in sparse networks", arxiv:0711.1452 * Martin Rosvall and Carl T. Bergstrom o "An information-theoretic framework for resolving community structure in complex networks", physics/0612035 [Or, MDL to the rescue!] o "Maps of Information Flow Reveal Community Structure In Complex Networks" [Thanks to Martin and Carl for a preprint] * Erin N. Sawardecker, Marta Sales-Pardo, Luís A. Nunes Amaral, "Detection of node group membership in networks with group overlap", arxiv:0812.1243 * Chayant Tantipathananandh, Tanya Berger-Wolf and David Kempe, "A Framework For Community Identification in Dynamic Social Networks" [PDF] * Joshua R. Tyler, Dennis M. Wilkinson and Bernardo A. Huberman, "Email as Spectroscopy: Automated Discovery of Community Structure within Organizations," cond-mat/0303264 * I. Vragovic and E. Louis, "Network community structure and loop coefficient method", Physical Review E 74 (2006): 016105 * Huijie Yang, Wenxu Wang, Tao Zhou, Binghong ang and Fangcui Zhao, "Reconstruct the Hierarchical Structure in a Complex Network", physics/0508026 ["Based upon the eigenvector centrality (EC) measure, a method is proposed to reconstruct the hierarchical structure of a complex network. It is tested on the Santa Fe Institute collaboration network, whose structure is well known."] * Haijun Zhou o "Distance, dissimilarity index, and network community structure," physics/0302032 o "Network Landscape from a Brownian Particle's Perspective," physics/0302030 * Etay Ziv, Manuel Middendorf and Chris Wiggins, "An Information-Theoretic Approach to Network Modularity", q-bio.QM/0411033

* Jim Bagrow and Erik Bollt, "A Local Method for Detecting Communities", cond-mat/0412482 * James Bagrow, Erik Bollt, Luciano da F. Costa, "Network Structure Revealed by Short Cycles", cond-mat/0612502 * S. Boccaletti, M. Ivanchenko, V. Latora, A. Pluchino and A. Rapisarda, "Dynamical clustering methods to find community structures", physics/0607179 * Michael James Bommarito II, Daniel Martin Katz, Jon Zelner, "On the Stability of Community Detection Algorithms on Longitudinal Citation Data", arxiv:0908.0449 * U. Brandes, D. Delling, M. Gaertler, R. Goerke, M. Hoefer, Z. Nikoloski, and D. Wagner, "Maximizing Modularity is hard", physics/0608255 [i.e., maximizing Newman's Q is NP hard. I haven't read beyond the abstract yet, so I don't know if they address the question of what makes it hard in the hard cases, and whether those are properties we should expect to see in real-world networks. Conceivably, actual social networks are, on average, easy to modularize...] * Andrea Capocci, Vito D. P. Servedio, Guido Caldarelli, Francesca Colaiori, "Detecting communities in large networks", cond-mat/0402499 * Horacio Castellini and Lilia Romanelli, "Social network from communities of electronic mail", nlin.CD/0509021 * Leon Danon, Albert Díaz-Guilera, and Alex Arenas, "The effect of size heterogeneity on community identification in complex networks", Journal of Statistical Mechanics: Theory and Experiment (2006): P11010 = physics/0601144 * Leon Danon, Albert Díaz-Guilera, Jordi Duch and Alex Arenas, "Comparing community structure identification", Journal of Statistical Mechanics: Theory and Experiment (2005): P09008 = cond-mat/0505245 * Jordi Duch and Alex Arenas, "Community detection in complex networks using extremal optimization", Physical Review E 72 (2005): 027104 * Illes J. Farkas, Daniel Abel, Gergely Palla, Tamas Vicsek, "Weighted network modules", cond-mat/0703706 * Sam Field, Kenneth A. Frank, Kathryn Schiller, Catherine Riegle-Crumb and Chandra Muller, "Identifying positions from affiliation networks: Preserving the duality of people and events", Social Networks 28 (2006): 97--123 * G. W. Flake, S. R. Lawrence, C. L. Giles and F. M. Coetzee, "Self-organization and identification of Web communities", IEEE Computer 36 (2002): 66--71 * Santo Fortunato, "Community detection in graphs", arxiv:0906.0612 * Santo Fortunato and Marc Bathélemy, "Resolution limit in community detection", physics/0607100 = cite>Proceedings of the National Academy of Sciences (USA) 104 (2007): 36--41 * Santo Fortunato and Claudio Castellano, "Community Structure in Graphs", arxiv:0712.2716 [Review paper; thanks to Ed Vielmetti for the pointer] * Santo Fortunato, Vito Latora and Massimo Marchiori, "A Method to Find Community Structures Based on Information Centrality", cond-mat/0402522 * Kenneth A. Frank, "Identifying Cohesive Subgroups", Social Networks 17 (1995): 27--56 * David Gfeller, Jean-Cédric Chappelier, and Paolo De Los Rios, "Finding instabilities in the community structure of complex networks", Physical Review E 72 (2005): 056135 * Rumi Ghosh, Kristina Lerman, "Structure of Heterogeneous Networks", arxiv:0906.2212 * V. Gol'dshtein and G. A. Koganov, "An indicator for community structure", physics/0607159 * Mark S. Handcock, Adrian E. Raftery and Jeremy Tantrum, "Model-Based Clustering for Social Networks" Journal of the Royal Statistical Society A 170 (2007): 301--354 [PDF preprint] * M. B. Hastings, "Community detection as an inference problem", Physical Review E 74 (2006): 035102 = cond-mat/0604429 * Erik Holmström, Nicolas Bock and Joan Brännlund, "Density Analysis of Network Community Divisions", cond-mat/0608612 * I. Ispolatov, I. Mazo, A. Yuryev, "Finding mesoscopic communities in sparse networks", q-bio.MN/0512038 = Journal of Statistical Mechanics (2006): P09014 * Brian Karrer, Elizaveta Levina, M. E. J. Newman, "Robustness of community structure in networks", arxiv:0709.2108 * Jussi M. Kumpula, Jari Saramaki, Kimmo Kaski, and Janos Kertesz, "Resolution limit in complex network community detection with Potts model approach",cond-mat/0610370 * Andrea Lancichinetti, Santo Fortunato, "Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities", arxiv:0904.3940 * Sune Lehmann, Martin Schwartz, Lars Kai Hansen, "Bi-clique Communities", arxiv:0710.4867 * Michele Leone, Sumedha, Martin Weigt, "Clustering by soft-constraint affinity propagation: Applications to gene-expression data", arxiv:0705.2646 * Claire P. Massen, Jonathan P. K. Doye, "Thermodynamics of Community Structure", cond-mat/0610077 * Ian X.Y. Leung, Pan Hui, Pietro Lio', Jon Crowcroft, "Towards Real Time Community Detection in Large Networks", arxiv:0808.2633 * Stefanie Muff, Francesco Rao, and Amedeo Caflisch, "Local modularity measure for network clusterizations", Physical Review E 72 (2005): 056107 * Andreas Noack, "Modularity clustering is force-directed layout", arxiv:0807.4052 * Gergely Palla, Imre Derenyi, Illes Farkas and Tamas Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society", Nature 435 (2005): 814--818 = physics/0506133 * Gergely Palla, Illes J. Farkas, Peter Pollner, Imre Derenyi, Tamas Vicsek, "Directed network modules", physics/0703248 * Nicolas Pissard and Houssem Assadi, "Detecting overlapping communities in linear time with P&A algorithm", physics/0509254 * Pascal Pons, "Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View", cs.DS/0608050 * Mason A. Porter, Jukka-Pekka Onnela, Peter J. Mucha, "Communities in Networks", arxiv:0902.3788 * Josep M. Pujol, Javier Béjar, and Jordi Delgado, "Clustering algorithm for determining community structure in large networks", Physical Review E 74 (2006): 016107 * Francisco A. Rodrigues, Gonzalo Travieso, Luciano da F. Costa, "Fast Community Identification by Hierarchical Growth", physics/0602144 * Huaijun Qiu and Edwin R. Hancock, "Graph matching and clustering using spectral partitions", Pattern Recognition 39 (2006): 22--34 [In this context, for the ideas on hierarchical decomposition, which sounds like it might work for community discovery, if in fact it's not equivalent to some existing community-discovery algorithm.] * Usha Nandini Raghavan, Reka Albert, Soundar Kumara, "Near linear time algorithm to detect community structures in large-scale networks", arxiv:0709.2938 ["every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have"] * Jörg Reichardt and Stefan Bornholdt, "When are networks truly modular?", cond-mat/0606220 * Jörg Reichardt and Michele Leone, "(Un)detectable cluster structure in sparse networks", arxiv:0711.1452 * Martin Rosvall and Carl T. Bergstrom o "An information-theoretic framework for resolving community structure in complex networks", physics/0612035 [Or, MDL to the rescue!] o "Maps of Information Flow Reveal Community Structure In Complex Networks" [Thanks to Martin and Carl for a preprint] * Erin N. Sawardecker, Marta Sales-Pardo, Luís A. Nunes Amaral, "Detection of node group membership in networks with group overlap", arxiv:0812.1243 * Chayant Tantipathananandh, Tanya Berger-Wolf and David Kempe, "A Framework For Community Identification in Dynamic Social Networks" [PDF] * Joshua R. Tyler, Dennis M. Wilkinson and Bernardo A. Huberman, "Email as Spectroscopy: Automated Discovery of Community Structure within Organizations," cond-mat/0303264 * I. Vragovic and E. Louis, "Network community structure and loop coefficient method", Physical Review E 74 (2006): 016105 * Huijie Yang, Wenxu Wang, Tao Zhou, Binghong ang and Fangcui Zhao, "Reconstruct the Hierarchical Structure in a Complex Network", physics/0508026 ["Based upon the eigenvector centrality (EC) measure, a method is proposed to reconstruct the hierarchical structure of a complex network. It is tested on the Santa Fe Institute collaboration network, whose structure is well known."] * Haijun Zhou o "Distance, dissimilarity index, and network community structure," physics/0302032 o "Network Landscape from a Brownian Particle's Perspective," physics/0302030 * Etay Ziv, Manuel Middendorf and Chris Wiggins, "An Information-Theoretic Approach to Network Modularity", q-bio.QM/0411033

THERE ARE MANY WAYS TO SKIN A CAT….. THERE ARE EVEN MORE WAYS TO FIND COMMUNITIES IN NETWORKS…..

* Erik Holmström, Nicolas Bock and Joan Brännlund, "Density Analysis of Network Community Divisions", cond-mat/0608612 * I. Ispolatov, I. Mazo, A. Yuryev, "Finding mesoscopic communities in sparse networks", q-bio.MN/0512038 = Journal of Statistical Mechanics (2006): P09014 * Brian Karrer, Elizaveta Levina, M. E. J. Newman, "Robustness of community structure in networks", arxiv:0709.2108 * Jussi M. Kumpula, Jari Saramaki, Kimmo Kaski, and Janos Kertesz, "Resolution limit in complex network community detection with Potts model approach",cond-mat/0610370 * Andrea Lancichinetti, Santo Fortunato, "Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities", arxiv:0904.3940 * Sune Lehmann, Martin Schwartz, Lars Kai Hansen, "Bi-clique Communities", arxiv:0710.4867 * Michele Leone, Sumedha, Martin Weigt, "Clustering by soft-constraint affinity propagation: Applications to gene-expression data", arxiv:0705.2646 * Claire P. Massen, Jonathan P. K. Doye, "Thermodynamics of Community Structure", cond-mat/0610077 * Ian X.Y. Leung, Pan Hui, Pietro Lio', Jon Crowcroft, "Towards Real Time Community Detection in Large Networks", arxiv:0808.2633 * Stefanie Muff, Francesco Rao, and Amedeo Caflisch, "Local modularity measure for network clusterizations", Physical Review E 72 (2005): 056107 * Andreas Noack, "Modularity clustering is force-directed layout", arxiv:0807.4052 * Gergely Palla, Imre Derenyi, Illes Farkas and Tamas Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society", Nature 435 (2005): 814--818 = physics/0506133 * Gergely Palla, Illes J. Farkas, Peter Pollner, Imre Derenyi, Tamas Vicsek, "Directed network modules", physics/0703248 * Nicolas Pissard and Houssem Assadi, "Detecting overlapping communities in linear time with P&A algorithm", physics/0509254 * Pascal Pons, "Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View", cs.DS/0608050 * Mason A. Porter, Jukka-Pekka Onnela, Peter J. Mucha, "Communities in Networks", arxiv:0902.3788 * Josep M. Pujol, Javier Béjar, and Jordi Delgado, "Clustering algorithm for determining community structure in large networks", Physical Review E 74 (2006): 016107 * Francisco A. Rodrigues, Gonzalo Travieso, Luciano da F. Costa, "Fast Community Identification by Hierarchical Growth", physics/0602144

* Huaijun Qiu and Edwin R. Hancock, "Graph matching and clustering using spectral partitions", Pattern Recognition 39 (2006): 22--34 [In this context, for the ideas on hierarchical decomposition, which sounds like it might work for community discovery, if in fact it's not equivalent to some existing community-discovery algorithm.] * Usha Nandini Raghavan, Reka Albert, Soundar Kumara, "Near linear time algorithm to detect community structures in large-scale networks", arxiv:0709.2938 ["every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have"] * Jörg Reichardt and Stefan Bornholdt, "When are networks truly modular?", cond-mat/0606220 * Jörg Reichardt and Michele Leone, "(Un)detectable cluster structure in sparse networks", arxiv:0711.1452 * Martin Rosvall and Carl T. Bergstrom o "An information-theoretic framework for resolving community structure in complex networks", physics/0612035 [Or, MDL to the rescue!] o "Maps of Information Flow Reveal Community Structure In Complex Networks" [Thanks to Martin and Carl for a preprint] * Erin N. Sawardecker, Marta Sales-Pardo, Luís A. Nunes Amaral, "Detection of node group membership in networks with group overlap", arxiv:0812.1243 * Chayant Tantipathananandh, Tanya Berger-Wolf and David Kempe, "A Framework For Community Identification in Dynamic Social Networks" [PDF] * Joshua R. Tyler, Dennis M. Wilkinson and Bernardo A. Huberman, "Email as Spectroscopy: Automated Discovery of Community Structure within Organizations," cond-mat/0303264 * I. Vragovic and E. Louis, "Network community structure and loop coefficient method", Physical Review E 74 (2006): 016105 * Huijie Yang, Wenxu Wang, Tao Zhou, Binghong ang and Fangcui Zhao, "Reconstruct the Hierarchical Structure in a Complex Network", physics/0508026 ["Based upon the eigenvector centrality (EC) measure, a method is proposed to reconstruct the hierarchical structure of a complex network. It is tested on the Santa Fe Institute collaboration network, whose structure is well known."] * Haijun Zhou o "Distance, dissimilarity index, and network community structure," physics/0302032 o "Network Landscape from a Brownian Particle's Perspective," physics/0302030 * Etay Ziv, Manuel Middendorf and Chris Wiggins, "An Information-Theoretic Approach to Network Modularity", q-bio.QM/0411033

Largest component of SFI collaborations

Add Health Data


© Leong Hon Wai

Outline of Talk

Large Networks are Everywhere Community Detection: A Quick Overview

Application in Computational Biology

Protein Complex Detection Specialized Algorithms Performance Evaluation Challenges and Conclusion

Goal is to minimize R

Adjacency Matrix

Families of Community FindingMethods / Algorithms

1 DIVISIVE METHODS

Newman, Girvan (2004)

ModularityWhen do you stop cutting?

eij is equal to the number of links between community i and community j.


It is important to recalculate


2 CLIQUE Percolation METHODS

Just google: “cfinder”

Wanna use Clique Percolation Method?

Also available online. Just google “BCFinder”


3 LINK CLUSTERING METHODS

“a group of densely interconnected nodes”

COMMUNITY:

Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann, "Communities and Hierarchical Organization of Links in Complex Networks", submitted, arxiv:0903.3178.

http://arxiv.org/abs/0903.3178

“a group of TOPOLOGICALLY SIMILAR LINKS”

COMMUNITY:



Family

Friends

Colleagues



Friends

Colleagues

‘Family’ links

Family



‘Family’ links

‘Friends’ links

Colleagues

FriendsFamily



Colleagues

‘Friends’ links

‘Nerds & geeks’ links

FriendsFamily

‘Family’ links



Node: multiple membership

Links: (almost) unique membership


© Leong Hon Wai

Thank you.Q & AContact:

Hon Wai Leong (梁汉槐 )FB, email: [email protected]

http://www.comp.nus.edu.sg/~leonghw/

algorithms for community detection in large networks (and guidelines on cs3230r)

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