GraphScope: parameter-free mining of large time-evolving graphs

How can we find communities in dynamic networks of social interactions, such as who calls whom, who emails whom, or who sells to whom? How can we spot discontinuity timepoints in such streams of graphs, in an on-line, any-time fashion? We propose GraphScope, that addresses both problems, using information theoretic principles. Contrary to the majority of earlier methods, it needs no user-defined parameters. Moreover, it is designed to operate on large graphs, in a streaming fashion. We demonstrate the efficiency and effectiveness of our GraphScope on real datasets from several diverse domains. In all cases it produces meaningful time-evolving patterns that agree with human intuition. Categories and Subject Descriptors H.2.8 [Database applications]: Data mining General Terms Algorithms

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Jimeng Sun, Christos Faloutsos, Spiros Papadimitriou, Philip S. Yu
• Keywords: Data Mining, General Terms Algorithms, Information Theoretic Principles, KDD 2007, Meaningful Time-evolving Patterns