Mining indirect antagonistic communities from social interactions

Antagonistic communities refer to groups of people with opposite tastes, opinions, and factions within a community. Given a set of interactions among people in a community, we develop a novel pattern mining approach to mine a set of antagonistic communities. In particular, based on a set of user-specified thresholds, we extract a set of pairs of communities that behave in opposite ways with one another. We focus on extracting a compact lossless representation based on the concept of closed patterns to prevent exploding the number of mined antagonistic communities. We also present a variation of the algorithm using a divide and conquer strategy to handle large datasets when main memory is inadequate. The scalability of our approach is tested on synthetic datasets of various sizes mined using various parameters. Case studies on Amazon, Epinions, and Slashdot datasets further show the efficiency and the utility of our approach in extracting antagonistic communities from social interactions.

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