Download presentation

Presentation is loading. Please wait.

Published byBlaine Foulkes Modified about 1 year ago

1
Hon Wai Leong, NUS (… Finding Communities) Page 1 © Leong Hon Wai, 2013 Leong Hon Wai ( 梁汉槐 ) Department of Computer Science National University of Singapore CS3230R Talk: 13 Feb 2014 Algorithms for Community Detection in Large Networks (And guidelines on CS3230R)

2
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 2 © 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

3
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 3 © 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) – ??

4
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 4 © Leong Hon Wai

5
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 5 © Leong Hon Wai Large Real-World Networks Internet graphs, WWW graphs Citation networks, actor networks Transportation network, networks Food Web, Social Networks (FB, Linked-In, etc) Biochemical networks Protein-Protein Interaction (PPI) networks

6
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 6 © Leong Hon Wai Community Structure (example)

7
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 7 © 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.

8
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 8 © 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”.

9
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 9 © Leong Hon Wai Community Structure (example)

10
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 10 © Leong Hon Wai Where is the Rabbit (Sept 2013) Typhoon Usagi ( ウサギ, rabbit) (16-24 Sept 2013)

11
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 11 © 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

12
* Recommended: Aaron Clauset, "Finding local community structure in networks", physics/ = Physical Review E 72 (2005): [Clever; but then, Aaron is clever.] * Aaron Clauset, M. E. J. Newman and Cristopher Moore, "Finding Community Structure in Very Large Networks", cond-mat/ = Physical Review E 70 (2004): * J.-J. Daudin, F. Picard and S. Robin, "A Mixture Model for Random Graphs", Statistics and Computing 18 (2008): * Michelle Girvan and M. E. J. Newman, "Community structure in social and biological networks," cond- mat/ = Proceedings of the National Academy of Sciences (USA) 99 (2002): * Roger Guimera, Marta Sales-Pardo and Luis A. N. Amaral, "Modularity from Fluctuations in Random Graphs", cond-mat/ = Physical Review E 70 (2004): * Jake M. Hofman, Chris H. Wiggins, "A Bayesian Approach to Network Modularity", arxiv: [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: [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: * M. E. J. Newman o "Modularity and community structure in networks", physics/ = Proceedings of the National Academy of Sciences (USA) 103 (2006): o "Finding community structure in networks using the eigenvectors of matrices", Physical Review E 74 (2006): = physics/ * M. E. J. Newman and Michelle Girvan o "Mixing patterns and community structure in networks", cond-mat/ o "Finding and evaluating community structure in networks", Physical Review E 69 (2003): = cond-mat/ * Jörg Reichardt and Stefan Bornholdt [Code is available by 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): = cond-mat/ o "Statistical Mechanics of Community Detection", cond-mat/ = Physical Review E 74 (2006): 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: [Discussion] * M. Sales-Pardo, R. Guimera, A. Moreira, L. Amaral, "Extracting the hierarchical organization of complex systems", arxiv: * Modesty forbids me to recommend: CRS, Marcelo F. Camperi and Kristina Lisa Klinkner, "Discovering Functional Communities in Dynamical Networks", q-bio.NC/ * To read: Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg and Eric P. Xing, "Mixed membership stochastic blockmodels", arxiv: * Nelson Augusto Alves, "Unveiling community structures in weighted networks", physics/ * Leonardo Angelini, Stefano Boccaletti, Daniele Marinazzo, Mario Pellicoro, and Sebastiano Stramaglia, "Fast identification of network modules by optimization of ratio association", cond-mat/ * L. Angelini, D. Marinazzo, M. Pellicoro and S. Stramaglia, "Natural clustering: the modularity approach", cond-mat/ * A. Arenas, J. Duch, A. Fernandez, S. Gomez, "Size reduction of complex networks preserving modularity", physics/ [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/ * Alex Arenas, Alberto Fernandez, Santo Fortunato, Sergio Gomez, "Motif-based communities in complex networks", arxiv: * Jim Bagrow and Erik Bollt, "A Local Method for Detecting Communities", cond-mat/ * James Bagrow, Erik Bollt, Luciano da F. Costa, "Network Structure Revealed by Short Cycles", cond- mat/ * S. Boccaletti, M. Ivanchenko, V. Latora, A. Pluchino and A. Rapisarda, "Dynamical clustering methods to find community structures", physics/ * Michael James Bommarito II, Daniel Martin Katz, Jon Zelner, "On the Stability of Community Detection Algorithms on Longitudinal Citation Data", arxiv: * U. Brandes, D. Delling, M. Gaertler, R. Goerke, M. Hoefer, Z. Nikoloski, and D. Wagner, "Maximizing Modularity is hard", physics/ [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/ * Horacio Castellini and Lilia Romanelli, "Social network from communities of electronic mail", nlin.CD/ * 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/ * 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/ * Jordi Duch and Alex Arenas, "Community detection in complex networks using extremal optimization", Physical Review E 72 (2005): * Illes J. Farkas, Daniel Abel, Gergely Palla, Tamas Vicsek, "Weighted network modules", cond- mat/ * 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): * G. W. Flake, S. R. Lawrence, C. L. Giles and F. M. Coetzee, "Self-organization and identification of Web communities", IEEE Computer 36 (2002): * Santo Fortunato, "Community detection in graphs", arxiv: * Santo Fortunato and Marc Bathélemy, "Resolution limit in community detection", physics/ = cite>Proceedings of the National Academy of Sciences (USA) 104 (2007): * Santo Fortunato and Claudio Castellano, "Community Structure in Graphs", arxiv: [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/ * Kenneth A. Frank, "Identifying Cohesive Subgroups", Social Networks 17 (1995): * David Gfeller, Jean-Cédric Chappelier, and Paolo De Los Rios, "Finding instabilities in the community structure of complex networks", Physical Review E 72 (2005): * Rumi Ghosh, Kristina Lerman, "Structure of Heterogeneous Networks", arxiv: * V. Gol'dshtein and G. A. Koganov, "An indicator for community structure", physics/ * Mark S. Handcock, Adrian E. Raftery and Jeremy Tantrum, "Model-Based Clustering for Social Networks" Journal of the Royal Statistical Society A 170 (2007): [PDF preprint] * M. B. Hastings, "Community detection as an inference problem", Physical Review E 74 (2006): = cond-mat/ * Erik Holmström, Nicolas Bock and Joan Brännlund, "Density Analysis of Network Community Divisions", cond-mat/ * I. Ispolatov, I. Mazo, A. Yuryev, "Finding mesoscopic communities in sparse networks", q- bio.MN/ = Journal of Statistical Mechanics (2006): P09014 * Brian Karrer, Elizaveta Levina, M. E. J. Newman, "Robustness of community structure in networks", arxiv: * Jussi M. Kumpula, Jari Saramaki, Kimmo Kaski, and Janos Kertesz, "Resolution limit in complex network community detection with Potts model approach",cond-mat/ * Andrea Lancichinetti, Santo Fortunato, "Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities", arxiv: * Sune Lehmann, Martin Schwartz, Lars Kai Hansen, "Bi-clique Communities", arxiv: * Michele Leone, Sumedha, Martin Weigt, "Clustering by soft-constraint affinity propagation: Applications to gene-expression data", arxiv: * Claire P. Massen, Jonathan P. K. Doye, "Thermodynamics of Community Structure", cond- mat/ * Ian X.Y. Leung, Pan Hui, Pietro Lio', Jon Crowcroft, "Towards Real Time Community Detection in Large Networks", arxiv: * Stefanie Muff, Francesco Rao, and Amedeo Caflisch, "Local modularity measure for network clusterizations", Physical Review E 72 (2005): * Andreas Noack, "Modularity clustering is force-directed layout", arxiv: * Gergely Palla, Imre Derenyi, Illes Farkas and Tamas Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society", Nature 435 (2005): = physics/ * Gergely Palla, Illes J. Farkas, Peter Pollner, Imre Derenyi, Tamas Vicsek, "Directed network modules", physics/ * Nicolas Pissard and Houssem Assadi, "Detecting overlapping communities in linear time with P&A algorithm", physics/ * Pascal Pons, "Post-Processing Hierarchical Community Structures: Quality Improvements and Multi- scale View", cs.DS/ * Mason A. Porter, Jukka-Pekka Onnela, Peter J. Mucha, "Communities in Networks", arxiv: * Josep M. Pujol, Javier Béjar, and Jordi Delgado, "Clustering algorithm for determining community structure in large networks", Physical Review E 74 (2006): * Francisco A. Rodrigues, Gonzalo Travieso, Luciano da F. Costa, "Fast Community Identification by Hierarchical Growth", physics/ * Huaijun Qiu and Edwin R. Hancock, "Graph matching and clustering using spectral partitions", Pattern Recognition 39 (2006): [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: ["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/ * Jörg Reichardt and Michele Leone, "(Un)detectable cluster structure in sparse networks", arxiv: * Martin Rosvall and Carl T. Bergstrom o "An information-theoretic framework for resolving community structure in complex networks", physics/ [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: * 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, " as Spectroscopy: Automated Discovery of Community Structure within Organizations," cond-mat/ * I. Vragovic and E. Louis, "Network community structure and loop coefficient method", Physical Review E 74 (2006): * Huijie Yang, Wenxu Wang, Tao Zhou, Binghong ang and Fangcui Zhao, "Reconstruct the Hierarchical Structure in a Complex Network", physics/ ["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/ o "Network Landscape from a Brownian Particle's Perspective," physics/ * Etay Ziv, Manuel Middendorf and Chris Wiggins, "An Information-Theoretic Approach to Network Modularity", q-bio.QM/ * Jim Bagrow and Erik Bollt, "A Local Method for Detecting Communities", cond-mat/ * James Bagrow, Erik Bollt, Luciano da F. Costa, "Network Structure Revealed by Short Cycles", cond- mat/ * S. Boccaletti, M. Ivanchenko, V. Latora, A. Pluchino and A. Rapisarda, "Dynamical clustering methods to find community structures", physics/ * Michael James Bommarito II, Daniel Martin Katz, Jon Zelner, "On the Stability of Community Detection Algorithms on Longitudinal Citation Data", arxiv: * U. Brandes, D. Delling, M. Gaertler, R. Goerke, M. Hoefer, Z. Nikoloski, and D. Wagner, "Maximizing Modularity is hard", physics/ [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/ * Horacio Castellini and Lilia Romanelli, "Social network from communities of electronic mail", nlin.CD/ * 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/ * 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/ * Jordi Duch and Alex Arenas, "Community detection in complex networks using extremal optimization", Physical Review E 72 (2005): * Illes J. Farkas, Daniel Abel, Gergely Palla, Tamas Vicsek, "Weighted network modules", cond- mat/ * 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): * G. W. Flake, S. R. Lawrence, C. L. Giles and F. M. Coetzee, "Self-organization and identification of Web communities", IEEE Computer 36 (2002): * Santo Fortunato, "Community detection in graphs", arxiv: * Santo Fortunato and Marc Bathélemy, "Resolution limit in community detection", physics/ = cite>Proceedings of the National Academy of Sciences (USA) 104 (2007): * Santo Fortunato and Claudio Castellano, "Community Structure in Graphs", arxiv: [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/ * Kenneth A. Frank, "Identifying Cohesive Subgroups", Social Networks 17 (1995): * David Gfeller, Jean-Cédric Chappelier, and Paolo De Los Rios, "Finding instabilities in the community structure of complex networks", Physical Review E 72 (2005): * Rumi Ghosh, Kristina Lerman, "Structure of Heterogeneous Networks", arxiv: * V. Gol'dshtein and G. A. Koganov, "An indicator for community structure", physics/ * Mark S. Handcock, Adrian E. Raftery and Jeremy Tantrum, "Model-Based Clustering for Social Networks" Journal of the Royal Statistical Society A 170 (2007): [PDF preprint] * M. B. Hastings, "Community detection as an inference problem", Physical Review E 74 (2006): = cond-mat/ * Erik Holmström, Nicolas Bock and Joan Brännlund, "Density Analysis of Network Community Divisions", cond-mat/ * I. Ispolatov, I. Mazo, A. Yuryev, "Finding mesoscopic communities in sparse networks", q- bio.MN/ = Journal of Statistical Mechanics (2006): P09014 * Brian Karrer, Elizaveta Levina, M. E. J. Newman, "Robustness of community structure in networks", arxiv: * Jussi M. Kumpula, Jari Saramaki, Kimmo Kaski, and Janos Kertesz, "Resolution limit in complex network community detection with Potts model approach",cond-mat/ * Andrea Lancichinetti, Santo Fortunato, "Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities", arxiv: * Sune Lehmann, Martin Schwartz, Lars Kai Hansen, "Bi-clique Communities", arxiv: * Michele Leone, Sumedha, Martin Weigt, "Clustering by soft-constraint affinity propagation: Applications to gene-expression data", arxiv: * Claire P. Massen, Jonathan P. K. Doye, "Thermodynamics of Community Structure", cond- mat/ * Ian X.Y. Leung, Pan Hui, Pietro Lio', Jon Crowcroft, "Towards Real Time Community Detection in Large Networks", arxiv: * Stefanie Muff, Francesco Rao, and Amedeo Caflisch, "Local modularity measure for network clusterizations", Physical Review E 72 (2005): * Andreas Noack, "Modularity clustering is force-directed layout", arxiv: * Gergely Palla, Imre Derenyi, Illes Farkas and Tamas Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society", Nature 435 (2005): = physics/ * Gergely Palla, Illes J. Farkas, Peter Pollner, Imre Derenyi, Tamas Vicsek, "Directed network modules", physics/ * Nicolas Pissard and Houssem Assadi, "Detecting overlapping communities in linear time with P&A algorithm", physics/ * Pascal Pons, "Post-Processing Hierarchical Community Structures: Quality Improvements and Multi- scale View", cs.DS/ * Mason A. Porter, Jukka-Pekka Onnela, Peter J. Mucha, "Communities in Networks", arxiv: * Josep M. Pujol, Javier Béjar, and Jordi Delgado, "Clustering algorithm for determining community structure in large networks", Physical Review E 74 (2006): * Francisco A. Rodrigues, Gonzalo Travieso, Luciano da F. Costa, "Fast Community Identification by Hierarchical Growth", physics/ * Huaijun Qiu and Edwin R. Hancock, "Graph matching and clustering using spectral partitions", Pattern Recognition 39 (2006): [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: ["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/ * Jörg Reichardt and Michele Leone, "(Un)detectable cluster structure in sparse networks", arxiv: * Martin Rosvall and Carl T. Bergstrom o "An information-theoretic framework for resolving community structure in complex networks", physics/ [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: * 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, " as Spectroscopy: Automated Discovery of Community Structure within Organizations," cond-mat/ * I. Vragovic and E. Louis, "Network community structure and loop coefficient method", Physical Review E 74 (2006): * Huijie Yang, Wenxu Wang, Tao Zhou, Binghong ang and Fangcui Zhao, "Reconstruct the Hierarchical Structure in a Complex Network", physics/ ["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/ o "Network Landscape from a Brownian Particle's Perspective," physics/ * Etay Ziv, Manuel Middendorf and Chris Wiggins, "An Information-Theoretic Approach to Network Modularity", q-bio.QM/ THERE ARE MANY WAYS TO SKIN A CAT….. THERE ARE EVEN MORE WAYS TO FIND COMMUNITIES IN NETWORKS…..

13
* Erik Holmström, Nicolas Bock and Joan Brännlund, "Density Analysis of Network Community Divisions", cond-mat/ * I. Ispolatov, I. Mazo, A. Yuryev, "Finding mesoscopic communities in sparse networks", q-bio.MN/ = Journal of Statistical Mechanics (2006): P09014 * Brian Karrer, Elizaveta Levina, M. E. J. Newman, "Robustness of community structure in networks", arxiv: * Jussi M. Kumpula, Jari Saramaki, Kimmo Kaski, and Janos Kertesz, "Resolution limit in complex network community detection with Potts model approach",cond- mat/ * Andrea Lancichinetti, Santo Fortunato, "Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities", arxiv: * Sune Lehmann, Martin Schwartz, Lars Kai Hansen, "Bi-clique Communities", arxiv: * Michele Leone, Sumedha, Martin Weigt, "Clustering by soft-constraint affinity propagation: Applications to gene-expression data", arxiv: * Claire P. Massen, Jonathan P. K. Doye, "Thermodynamics of Community Structure", cond-mat/ * Ian X.Y. Leung, Pan Hui, Pietro Lio', Jon Crowcroft, "Towards Real Time Community Detection in Large Networks", arxiv: * Stefanie Muff, Francesco Rao, and Amedeo Caflisch, "Local modularity measure for network clusterizations", Physical Review E 72 (2005): * Andreas Noack, "Modularity clustering is force-directed layout", arxiv: * Gergely Palla, Imre Derenyi, Illes Farkas and Tamas Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society", Nature 435 (2005): = physics/ * Gergely Palla, Illes J. Farkas, Peter Pollner, Imre Derenyi, Tamas Vicsek, "Directed network modules", physics/ * Nicolas Pissard and Houssem Assadi, "Detecting overlapping communities in linear time with P&A algorithm", physics/ * Pascal Pons, "Post-Processing Hierarchical Community Structures: Quality Improvements and Multi-scale View", cs.DS/ * Mason A. Porter, Jukka-Pekka Onnela, Peter J. Mucha, "Communities in Networks", arxiv: * Josep M. Pujol, Javier Béjar, and Jordi Delgado, "Clustering algorithm for determining community structure in large networks", Physical Review E 74 (2006): * Francisco A. Rodrigues, Gonzalo Travieso, Luciano da F. Costa, "Fast Community Identification by Hierarchical Growth", physics/ * Huaijun Qiu and Edwin R. Hancock, "Graph matching and clustering using spectral partitions", Pattern Recognition 39 (2006): [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: ["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/ * Jörg Reichardt and Michele Leone, "(Un)detectable cluster structure in sparse networks", arxiv: * Martin Rosvall and Carl T. Bergstrom o "An information-theoretic framework for resolving community structure in complex networks", physics/ [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: * 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, " as Spectroscopy: Automated Discovery of Community Structure within Organizations," cond-mat/ * I. Vragovic and E. Louis, "Network community structure and loop coefficient method", Physical Review E 74 (2006): * Huijie Yang, Wenxu Wang, Tao Zhou, Binghong ang and Fangcui Zhao, "Reconstruct the Hierarchical Structure in a Complex Network", physics/ ["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/ o "Network Landscape from a Brownian Particle's Perspective," physics/ * Etay Ziv, Manuel Middendorf and Chris Wiggins, "An Information-Theoretic Approach to Network Modularity", q-bio.QM/

14
Largest component of SFI collaborations

15

16
Add Health Data

17
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 17 © 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

18
Goal is to minimize R Adjacency Matrix

19

20
Families of Community Finding Methods / Algorithms 1 DIVISIVE METHODS

21

22

23
Newman, Girvan (2004) Modularity When do you stop cutting? e ij is equal to the number of links between community i and community j.

24
Newman, Girvan (2004) It is important to recalculate

25
Newman, Girvan (2004)

26
Families of Community Finding Methods / Algorithms 2 CLIQUE Percolation METHODS

27

28

29

30
Just google: “cfinder” Wanna use Clique Percolation Method?

31

32

33

34
Also available online. Just google “BCFinder”

35
Families of Community Finding Methods / Algorithms 3 LINK CLUSTERING METHODS

36
“a group of densely interconnected nodes” Topologically Similar LINKS COMMUNITY: Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann, "Communities and Hierarchical Organization of Links in Complex Networks", submitted, arxiv: arxiv:

37
“a group of TOPOLOGICALLY SIMILAR LINKS ” Topologically Similar LINKS COMMUNITY: Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann, "Communities and Hierarchical Organization of Links in Complex Networks", submitted, arxiv: arxiv:

38
Family Friends Colleagues Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann, "Communities and Hierarchical Organization of Links in Complex Networks", submitted, arxiv: arxiv:

39
Friends Colleagues ‘Family’ links Family Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann, "Communities and Hierarchical Organization of Links in Complex Networks", submitted, arxiv: arxiv:

40
‘Family’ links ‘Friends’ links Colleagues Friends Family Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann, "Communities and Hierarchical Organization of Links in Complex Networks", submitted, arxiv: arxiv:

41
Colleagues ‘Friends’ links ‘Nerds & geeks’ links Friends Family ‘Family’ links Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann, "Communities and Hierarchical Organization of Links in Complex Networks", submitted, arxiv: arxiv:

42
Node: multiple membership Links: (almost) unique membership

43

44

45
Hon Wai Leong, Computer Science, NUS (PPI Complex Detection, Sep 2013) Page 45 © Leong Hon Wai Thank you. Q & A Contact: Hon Wai Leong ( 梁汉槐 ) FB,

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google