Extracting Information from Negative Interactions in Multiplex Networks Using Mutual Information
Many interesting real-world systems are represented as complex networks with multiple types of interactions and complicated dependency structures between layers. These interactions can be encoded as having a valence with positive links marking interactions such as trust and friendship and negative links denoting distrust or hostility. Extracting information from these negative interactions is challenging since standard topological metrics are often poor predictors of negative link formation, particularly across network layers. In this paper, we introduce a method based on mutual information which enables us to predict both negative and positive relationships. Our experiments show that SMLP (Signed Multiplex Link Prediction) can leverage negative relationship layers in multiplex networks to improve link prediction performance.
KeywordsMultiplex link prediction Complex networks Mutual information
Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. The Travian dataset was provided by Drs. Rolf T. Wigand and Nitin Agarwal (University of Arkansas at Little Rock, Department of Information Science).
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