On detecting maximal quasi antagonistic communities in signed graphs

• Main Authors: GAO, Ming, LIM, Ee-Peng, LO, David, PRASETYO, Philips Kokoh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Bi-clique; Enumeration tree; Power law distribution; Quasi antagonistic community; Signed graph; Computer Sciences; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/2858; https://ink.library.smu.edu.sg/context/sis_research/article/3858/viewcontent/Detecting_maximal_quasi_antagonistic_communities_in_signed_graphs_afv.pdf
• Institution: Singapore Management University

Many networks can be modeled as signed graphs. These include social networks, and relationships/interactions networks. Detecting sub-structures in such networks helps us understand user behavior, predict links, and recommend products. In this paper, we detect dense sub-structures from a signed graph...