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Node Classification for Signed Social Networks Using Diffuse Interface Methods

  • Pedro MercadoEmail author
  • Jessica Bosch
  • Martin Stoll
Conference paper
  • 188 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11906)

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.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of TübingenTübingenGermany
  2. 2.Department of Computer ScienceThe University of British ColumbiaVancouverCanada
  3. 3.Faculty of MathematicsTechnische Universität ChemnitzChemnitzGermany

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