Online Community Detection by Using Nearest Hubs

Community and cluster detection is a popular field of social network analysis. Most algorithms focus on static graphs or series of snapshots. In this paper we present an algorithm, which detects communities in dynamic graphs. The method is based on shortest paths to high-connected nodes, so called hubs. Due to local message passing we can update the clustering results with low computational power. The presented algorithm is compared with other for some static social networks. The reached modularity is not as high as the Louvain method, but even higher then spectral clustering. For large-scale real-world datasets with given ground truth, we could reconstruct most of the given community structure. The advantage of the algorithm is the good performance in dynamic scenarios.

[1]  A. Hoffman,et al.  Lower bounds for the partitioning of graphs , 1973 .

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

[3]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[4]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[6]  Jianbo Shi,et al.  Multiclass spectral clustering , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Jure Leskovec,et al.  Defining and Evaluating Network Communities Based on Ground-Truth , 2012, ICDM.

[8]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[9]  Beom Jun Kim,et al.  Growing scale-free networks with tunable clustering. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Myra Spiliopoulou,et al.  Studying Community Dynamics with an Incremental Graph Mining Algorithm , 2008, AMCIS.

[11]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[13]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.

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

[15]  Reka Albert,et al.  Mean-field theory for scale-free random networks , 1999 .

[16]  Mansoureh Takaffoli,et al.  Community Evolution Mining in Dynamic Social Networks , 2011 .

[17]  Julia Hirschberg,et al.  V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure , 2007, EMNLP.