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

  • Authors:
  • Jimeng Sun;Christos Faloutsos;Spiros Papadimitriou;Philip S. Yu

  • Affiliations:
  • Carnegie Mellon University;Carnegie Mellon University;IBM TJ Watson Research Center;IBM TJ Watson Research Center

  • Venue:
  • Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2007

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Abstract

How can we find communities in dynamic networks of socialinteractions, such as who calls whom, who emails whom, or who sells to whom? How can we spot discontinuity time-points 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.