Fast Algorithm for Modularity-Based Graph Clustering

In AI and Web communities, modularity-based graph clustering algorithms are being applied to various applications. However, existing algorithms are not applied to large graphs because they have to scan all vertices/edges iteratively. The goal of this paper is to efficiently compute clusters with high modularity from extremely large graphs with more than a few billion edges. The heart of our solution is to compute clusters by incrementally pruning unnecessary vertices/edges and optimizing the order of vertex selections. Our experiments show that our proposal outperforms all other modularity-based algorithms in terms of computation time, and it finds clusters with high modularity.

[1]  Marco Rosa,et al.  Layered label propagation: a multiresolution coordinate-free ordering for compressing social networks , 2010, WWW.

[2]  Toshio Uchiyama,et al.  Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy , 2012, SEMWEB.

[3]  Arnaud Browet,et al.  Community Detection for Hierarchical Image Segmentation , 2011, IWCIA.

[4]  Ken Wakita,et al.  Finding community structure in mega-scale social networks: [extended abstract] , 2007, WWW '07.

[5]  Yasuhiro Fujiwara,et al.  Efficient personalized pagerank with accuracy assurance , 2012, KDD.

[6]  Matthieu Latapy,et al.  Basic notions for the analysis of large two-mode networks , 2008, Soc. Networks.

[7]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  S.,et al.  An Efficient Heuristic Procedure for Partitioning Graphs , 2022 .

[9]  K. Fernow.  New York , 1896, American Potato Journal.

[10]  Jae Hoon Choi,et al.  Factors Affecting End-User Satisfaction on Facebook , 2012, ICWSM.

[11]  Yiming Yang,et al.  A study of retrospective and on-line event detection , 1998, SIGIR '98.

[12]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[14]  Michael R. Lyu,et al.  UserRec: A User Recommendation Framework in Social Tagging Systems , 2010, AAAI.

[15]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[16]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[17]  Yu Wang,et al.  Making Human Connectome Faster: GPU Acceleration of Brain Network Analysis , 2010, 2010 IEEE 16th International Conference on Parallel and Distributed Systems.

[18]  Michalis Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.

[19]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

[20]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Bu-Sung Lee,et al.  Event Detection in Twitter , 2011, ICWSM.

[22]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..