A Model of Consistent Node Types in Signed Directed Social Networks

TL;DR

This paper introduces a novel node type model for signed directed social networks that improves edge sign inference, especially when nodes lack common neighbors, outperforming existing methods on large real-world datasets.

Contribution

The paper develops a new node type model that explains edge signs without relying on common neighbors, extending structural balance and social status theories.

Findings

Better edge sign prediction accuracy on real-world networks

Effective modeling of node types in large signed directed graphs

Extension of existing theories to include directed triads

Abstract

Signed directed social networks, in which the relationships between users can be either positive (indicating relations such as trust) or negative (indicating relations such as distrust), are increasingly common. Thus the interplay between positive and negative relationships in such networks has become an important research topic. Most recent investigations focus upon edge sign inference using structural balance theory or social status theory. Neither of these two theories, however, can explain an observed edge sign well when the two nodes connected by this edge do not share a common neighbor (e.g., common friend). In this paper we develop a novel approach to handle this situation by applying a new model for node types. Initially, we analyze the local node structure in a fully observed signed directed network, inferring underlying node types. The sign of an edge between two nodes must be consistent with their types; this explains edge signs well even when there are no common neighbors. We show, moreover, that our approach can be extended to incorporate directed triads, when they exist, just as in models based upon structural balance or social status theory. We compute Bayesian node types within empirical studies based upon partially observed Wikipedia, Slashdot, and Epinions networks in which the largest network (Epinions) has 119K nodes and 841K edges. Our approach yields better performance than state-of-the-art approaches for these three signed directed networks.