Mining Antagonistic Communities from Social NetworksMining Antagonistic Communities from Social Networks: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I
Publication TypeConference Proceeding Article
AbstractDuring 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 of our technique in extracting interesting information on antagonistic sub-communities.
City or CountryHyderabad
Creative Commons LicenseCreative Commons Attribution-Noncommercial-No Derivative Works 4.0
Citation InformationKuan ZHANG, David LO and Ee Peng LIM. "Mining Antagonistic Communities from Social Networks" Mining Antagonistic Communities from Social Networks: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I Vol. 6118 (2010) p. 68 - 80
Available at: http://works.bepress.com/david_lo/48/