ABSTRACT
Graphs or networks can be used to model complex systems. Detecting community structures from large network data is a classic and challenging task. In this paper, we propose a novel community detection algorithm, which utilizes a dynamic process by contradicting the network topology and the topology-based propinquity, where the propinquity is a measure of the probability for a pair of nodes involved in a coherent community structure. Through several rounds of mutual reinforcement between topology and propinquity, the community structures are expected to naturally emerge. The overlapping vertices shared between communities can also be easily identified by an additional simple postprocessing. To achieve better efficiency, the propinquity is incrementally calculated. We implement the algorithm on a vertex-oriented bulk synchronous parallel(BSP) model so that the mining load can be distributed on thousands of machines. We obtained interesting experimental results on several real network data.
Supplemental Material
- S. Boccaletti, M. Ivanchenko, V. Latora, A. Pluchino, and A. Rapisarda. Detecting complex network modularity by dynamical clustering. Phys Rev E Stat Nonlin Soft Matter Phys, 75(4), 2007.Google Scholar
- A. Capocci, V. D. P. Servedio, G. Caldarelli, and F. Colaiori. Detecting communities in large networks. Physica A: Statistical and Theoretical Physics, 352(2-4):669--676, July 2005.Google ScholarCross Ref
- A. Clauset, M. E. J. Newman, and C. Moore. Finding community structure in very large networks. Physical Review E, 70:066111, 2004.Google ScholarCross Ref
- L. Donetti and M. A. Munoz. Detecting network communities: a new systematic and efficient algorithm. Journal of Statistical Mechanics: Theory and Experiment, 2004(10):P10012, 2004.Google ScholarCross Ref
- J. Duch and A. Arenas. Community detection in complex networks using extremal optimization. Physical Review E, 72:027104, 2005.Google ScholarCross Ref
- S. Fortunato and C. Castellano. Community structure in graphs. Chapter of Springer's Encyclopedia of Complexity and System Science, Dec 2007.Google Scholar
- S. Fortunato, V. Latora, and M. Marchiori. Method to find community structures based on information centrality. Phys. Rev. E, 70(5):056104, Nov 2004.Google ScholarCross Ref
- M. Girvan and M. E. Newman. Community structure in social and biological networks. Proc Natl Acad Sci U S A, 99(12):7821--7826, June 2002.Google ScholarCross Ref
- R. Guimer¼a, M. Sales-Pardo, and L. A. N. Amaral. Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E, 70(2):025101, Aug 2004.Google ScholarCross Ref
- B. W. Kernighan and S. Lin. An efficient heuristic procedure for partitioning graphs. The Bell system technical journal, 49(1):291--307, 1970.Google Scholar
- M. E. J. Newman. Fast algorithm for detecting community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys, 69(6), 2004.Google Scholar
- M. E. J. Newman. Finding community structure in networks using the eigenvectors of matrices. Phys Rev E Stat Nonlin Soft Matter Phys, 74(3), 2006.Google Scholar
- M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review E, 69:026113, 2004.Google ScholarCross Ref
- G. Palla, I. Derenyi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043):814--818.Google ScholarCross Ref
- J. Pei, D. Jiang, and A. Zhang. On mining cross-graph quasi-cliques. In Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining, pages 228--238, New York, NY, USA, 2005. ACM. Google ScholarDigital Library
- P. Pons and M. Latapy. Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications, 10(2):191--218, Dec 2006.Google ScholarCross Ref
- F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi. Defining and identifying communities in networks. In Proceedings of the National Academy of Science of the United States of America, volume 101-9, pages 2658--2663, 2004.Google ScholarCross Ref
- J. Reichardt and S. Bornholdt. Statistical mechanics of community detection. Phys Rev E Stat Nonlin Soft Matter Phys, Mar 2006.Google ScholarCross Ref
- Wikipedia. Bulk synchronous parallel | wikipedia, the free encyclopedia, 2008. {Online; accessed 23-December-2008}.Google Scholar
- Z. Zeng, J. Wang, L. Zhou, and G. Karypis. Out-of-core coherent closed quasi-clique mining from large dense graph databases. ACM Trans. Database Syst., 32(2):13, 2007. Google ScholarDigital Library
Index Terms
- Parallel community detection on large networks with propinquity dynamics
Recommendations
Parallel community detection for massive graphs
PPAM'11: Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part ITackling the current volume of graph-structured data requires parallel tools. We extend our work on analyzing such massive graph data with the first massively parallel algorithm for community detection that scales to current data sizes, scaling to ...
Scalable distributed Louvain algorithm for community detection in large graphs
AbstractCommunity detection (or clustering) in large-scale graphs is an important problem in graph mining. Communities reveal interesting organizational and functional characteristics of a network. Louvain algorithm is an efficient sequential algorithm ...
A parallel multi-objective evolutionary algorithm for community detection in large-scale complex networks
AbstractCommunity detection in large-scale complex networks has recently received significant attention as the volume of available data is becoming larger. The use of evolutionary algorithms (EAs) for community detection in large-scale ...
Comments