Mining Antagonistic Communities From Social Networks - ZHANG, Kuan (Institutional Knowledge at Singapore Management University, 2010)
In this thesis, we examine the problem of mining antagonistic communities from social networks. In social networks, people with opposite opinions normally behave differently and form sub-communities each of which containing people sharing some common behaviors. In one scenario, people with opposite opinions show differences in their views on a set of items. Another scenario is people explicitly expressing whom they agree with, like or trust as well as whom they disagree with, dislike or distrust. We defined the indirect and direct antagonistic groups based on the two scenarios. We have developed algorithms to mine the two types of antagonistic groups. For indirect antagonistic group mining, our algorithm explores the search space of all the possible antagonistic groups starting from antagonistic groups of size two, followed by searching antagonistic groups of larger sizes. We have also developed a divide and conquer strategy to ensure our algorithm runs on large databases. We have conducted experiments on both synthetic datasets and real datasets. The results show that our approach can efficiently compute indirect antagonistic groups. Our experiments on the four real datasets show the utility of our work in extracting interesting information from real datasets. For direct antagonistic group mining, we have combined several existing algorithm blocks to mine the patterns. We found significant differences in behaviors of users showing antagonistic relationships and those showing friendship relationships.