2018/04/19 by Alireza Hajibagheri, Hajibagheri, Alireza, Gita Sukthankar +1
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1804.07210
arXiv admin note: substantial text overlap with arXiv:1609.03946, text overlap with arXiv:1003.2429 by other authors
openalex publication_date 2018/04/19 · arxiv created 2018/08/16 · arxiv updated 2018/08/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.