Investigating Negative Interactions in Multiplex Networks: A Mutual
Information Approach
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by
Alireza Hajibagheri, Gita Sukthankar
2018
Abstract
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.
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