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2007
ACM

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

10 years 6 months ago
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
Jimeng Sun, Christos Faloutsos, Spiros Papadimitri
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2007
Where KDD
Authors Jimeng Sun, Christos Faloutsos, Spiros Papadimitriou, Philip S. Yu
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