  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.
