Applications of Structural Balance in Signed Social Networks

TL;DR

This paper introduces new methods leveraging structural balance in signed social networks for analysis, visualization, and link prediction, using signed graph Laplacian and resistance distances, validated on four datasets.

Contribution

It presents novel signed network analysis techniques based on structural balance, including measures, community detection, visualization, and link prediction algorithms.

Findings

Methods effectively measure partial balance.

Community detection aligns with structural balance.

Algorithms perform well on four datasets.

Abstract

We present measures, models and link prediction algorithms based on the structural balance in signed social networks. Certain social networks contain, in addition to the usual 'friend' links, 'enemy' links. These networks are called signed social networks. A classical and major concept for signed social networks is that of structural balance, i.e., the tendency of triangles to be 'balanced' towards including an even number of negative edges, such as friend-friend-friend and friend-enemy-enemy triangles. In this article, we introduce several new signed network analysis methods that exploit structural balance for measuring partial balance, for finding communities of people based on balance, for drawing signed social networks, and for solving the problem of link prediction. Notably, the introduced methods are based on the signed graph Laplacian and on the concept of signed resistance distances. We evaluate our methods on a collection of four signed social network datasets.