HCDF: A Hybrid Community Discovery Framework

We introduce a novel Bayesian framework for hybrid community discovery in graphs. Our framework, HCDF (short for Hybrid Community Discovery Framework), can effectively incorporate hints from a number of other community detection algorithms and produce results that outperform the constituent parts. We describe two HCDF-based approaches which are: (1) effective, in terms of link prediction performance and robustness to small perturbations in network structure; (2) consistent, in terms of effectiveness across various application domains; (3) scalable to very large graphs; and (4) nonparametric. Our extensive evaluation on a collection of diverse and large real-world graphs, with millions of links, show that our HCDF-based approaches (a) achieve up to 0.22 improvement in link prediction performance as measured by area under ROC curve (AUC), (b) never have an AUC that drops below 0.91 in the worst case, and (c) find communities that are robust to small perturbations of the network structur...

• Type: Conference
• Year: 2010
• Where: SDM
• Authors: Keith Henderson, Tina Eliassi-Rad, Spiros Papadimitriou, Christos Faloutsos
• Keywords: Data Mining, Hybrid Community Discovery, Link Prediction Performance, SDM 2010, Small Perturbations