Edge-centric multi-view network representation for link mining in signed social networks

Abstract

Signed social networks which are using both negative and positive links are becoming a popular form of social networks. This paper seeks to gain insight into how a user creates a positive or negative connection towards other users. The sign of connections between the users can be predicted efficiently and reliably using a newly proposed metric. In this paper, a new representation which is named Inverse square Metric is proposed. Inverse square metrics is inspired from Inverse square law which uses node properties and distance-based metrics to represent a connection in social networks.Inverse square metric measures the importance and intensity of a pair of nodes using node properties and defines the distance between two nodes as penalty. 16 variant of networks are created to compute the distance between two nodes in the network.In this study, two main contributions are made. First, a new metric to represent edges is proposed which uses both neighbourhood and distance-based link prediction measures.In addition, a unified framework for sign and link prediction problem based on the new representation is proposed. The experimental results on a group of six large networks including Amazon, Facebook, arXiv ASTRO-PH, Epinions, Slashdot, and Wikipedia prove the effectiveness of the proposed method.