Node Classification for Signed Social Networks Using Diffuse Interface Methods
This addresses the problem of node classification in signed social networks, which is incremental as it extends existing methods to a less explored domain.
The paper tackled node classification in signed social networks by using diffuse interface methods based on the Ginzburg-Landau functional and extended graph Laplacians, showing that incorporating both positive and negative interactions improves performance and consistently outperforms the state of the art.
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.