Low rank modeling of signed networks

Trust networks, where people leave trust and distrust feedback, are becoming increasingly common. These networks may be regarded as signed graphs, where a positive edge weight captures the degree of trust while a negative edge weight captures the degree of distrust. Analysis of such signed networks has become an increasingly important research topic. One important analysis task is that of sign inference, i.e., infer unknown (or future) trust or distrust relationships given a partially observed signed network. Most state-of-the-art approaches consider the notion of structural balance in signed networks, building inference algorithms based on information about links, triads, and cycles in the network. In this paper, we first show that the notion of weak structural balance in signed networks naturally leads to a global low-rank model for the network. Under such a model, the sign inference problem can be formulated as a low-rank matrix completion problem. We show that we can perfectly re...

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Cho-Jui Hsieh, Kai-Yang Chiang, Inderjit S. Dhillon
• Keywords: Art Matrix, Data Mining, Inference Problem, KDD 2012, Matrix Completion