Social balance theory defines balance or imbalance of sentiment relation in relations with social network theory. Sentiments can result in the emergence of two groups, where discrepancy can exist between two groups. Social and content networks model the community dynamics. We ask the question: "How can we describe and identify inequity and bias in a community?" We answer with an innovative scalable graph processing algorithm that scales social balance theory to automatically quantify disbalance and to predict outcomes in social and content networks.
We expand the balance theory to signed social network graph analysis and propose a frustration cloud view of the signed graph. We then quantify vertex and edge int erms of frustration cloud statistics, and validate this novel social network graphs analysis approach.
Community Discovery approaches suffer for lack of scalability or reproducability, and high modularity seems to be hard to overcome. We conduct a detailed analysis in terms of efficacy, efficiency, scalability, and reproducability of existing methods, and propose frustration cloud based approach for cluster boosting for high modular signed graphs.
