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Signed networks contain both positive and negative kinds of interactions likefriendship and enmity. The task of node classification in non-signed graphs hasproven to be beneficial in many real world applications, yet extensions tosigned networks remain largely unexplored. In this paper we introduce the firstanalysis of node classification in signed social networks via diffuse interfacemethods based on the Ginzburg-Landau functional together with differentextensions of the graph Laplacian to signed networks. We show that blending theinformation from both positive and negative interactions leads to performanceimprovement in real signed social networks, consistently outperforming thecurrent state of the art.

Node Classification for Signed Social Networks Using Diffuse Interface Methods