Mining direct antagonistic communities in explicit trust networks

• Main Authors: LO, David, SURIAN, Didi, KUAN, Zhang, LIM, Ee Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Direct antagonistic community; Mining maximal bi-cliques; Signed social network; Databases and Information Systems; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/1398; https://ink.library.smu.edu.sg/context/sis_research/article/2397/viewcontent/cikm11.pdf
• Institution: Singapore Management University

There has been a recent increase of interest in analyzing trust and friendship networks to gain insights about relationship dynamics among users. Many sites such as Epinions, Facebook, and other social networking sites allow users to declare trusts or friendships between different members of the community. In this work, we are interested in extracting direct antagonistic communities (DACs) within a rich trust network involving trusts and distrusts. Each DAC is formed by two subcommunities with trust relationships among members of each sub-community but distrust relationships across the sub-communities. We develop an efficient algorithm that could analyze large trust networks leveraging the unique property of direct antagonistic community. We have experimented with synthetic and real data-sets (myGamma and Epinions) to demonstrate the scalability of our proposed solution.