Mining direct antagonistic communities in signed social networks

• Main Authors: LO, David, SURIAN, Didi, PRASETYO, Philips Kokoh, KUAN, Zhang, LIM, Ee Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• 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/1691; https://ink.library.smu.edu.sg/context/sis_research/article/2690/viewcontent/Mining_direct_antagonistic_communities_in_signed_social_networks_afv.pdf
• Institution: Singapore Management University

Social networks provide a wealth of data to study relationship dynamics among people. Most social networks such as Epinions and Facebook allow users to declare trusts or friendships with other users. Some of them also allow users to declare distrusts or negative relationships. When both positive and...