Node Classification for Signed Social Networks Using Diffuse Interface Methods

TL;DR

This paper introduces a novel approach for node classification in signed social networks using diffuse interface methods based on the Ginzburg-Landau functional, demonstrating improved performance over existing methods.

Contribution

It is the first to analyze node classification in signed networks with diffuse interface methods and extended graph Laplacians, showing performance gains.

Findings

Blending positive and negative interactions improves classification accuracy.

The proposed method outperforms current state-of-the-art techniques.

Extensions of the graph Laplacian enhance model effectiveness.

Abstract

Signed networks contain both positive and negative kinds of interactions like friendship and enmity. The task of node classification in non-signed graphs has proven to be beneficial in many real world applications, yet extensions to signed networks remain largely unexplored. In this paper we introduce the first analysis of node classification in signed social networks via diffuse interface methods based on the Ginzburg-Landau functional together with different extensions of the graph Laplacian to signed networks. We show that blending the information from both positive and negative interactions leads to performance improvement in real signed social networks, consistently outperforming the current state of the art.