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 of our technique in extracting interesting information on antagonistic sub-communities.
Unable to display preview. Download preview PDF.
- 2.Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of International Conference on Very Large Data Bases (1994)Google Scholar
- 4.Dasgupta, I.: ‘living’ wage, class conflict and ethnic strife. Journal of Economic Behavior & Organization (2009) (in press)Google Scholar
- 7.Ding, B., Lo, D., Han, J., Khoo, S.-C.: Efficient mining of closed repetitive gapped subsequences from a sequence database. In: ICDE (2009)Google Scholar
- 9.Gibson, D., Kleinberg, J., Raghavan, P.: Inferring web communities from link topology. In: Hypertext (1998)Google Scholar
- 12.Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: WWW (2004)Google Scholar
- 13.Kunegis, J., Lommatzsch, A., Bauckhage, C.: The slashdot zoo: Mining a social network with negative edges. In: WWW (2009)Google Scholar
- 15.Liu, H., Lim, E.-P., Lauw, H., Le, M.-T., Sun, A., Srivastava, J., Kim, Y.: Predicting trusts among users of online communities: an epinions case study. In: EC (2008)Google Scholar
- 18.Wang, J., Han, J.: BIDE: Efficient mining of frequent closed sequences. In: ICDE (2004)Google Scholar