Node Classification for Signed Social Networks Using Diffuse Interface Methods
- Resource Type
- Working Paper
- Authors
- Mercado, Pedro; Bosch, Jessica; Stoll, Martin
- Source
- Subject
- Computer Science - Social and Information Networks
Computer Science - Machine Learning
Mathematics - Numerical Analysis
Statistics - Machine Learning
- Language
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.
Comment: Accepted at ECML-PKDD 2019