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PAKDD
2010
ACM

Mining Antagonistic Communities from Social Networks

11 years 10 months ago
Mining Antagonistic Communities from Social Networks
During social interactions in a community, there are often sub-communities that behave in opposite manner. These antagonistic sub-communities could represent groups of people with opposite tastes, factions within a community distrusting one another, etc. Taking as input a set of interactions within a community, we develop a novel pattern mining approach that extracts for a set of antagonistic sub-communities. In particular, based on a set of user specified thresholds, we extract a set of pairs of sub-communities that behave in opposite ways with one another. To prevent a blow up in these set of pairs, we focus on extracting a compact lossless representation based on the concept of closed patterns. To test the scalability of our approach, we built a synthetic data generator and experimented on the scalability of the algorithm when the size of the dataset and mining parameters are varied. Case studies on an Amazon book rating dataset show the efficiency of our approach and the utility o...
Kuan Zhang, David Lo, Ee-Peng Lim
Added 20 Jul 2010
Updated 20 Jul 2010
Type Conference
Year 2010
Where PAKDD
Authors Kuan Zhang, David Lo, Ee-Peng Lim
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