A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks

نویسندگان

چکیده

Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools network embedding, link prediction, and node classification. In this paper, we propose Hybrid Membership-Latent Distance Model (HM-LDM) by exploring how Latent (LDM) can be constrained to latent simplex. By controlling edge lengths corners simplex, volume space systematically controlled. Thereby communities are revealed as becomes more constrained, with hard memberships being recovered simplex goes zero. We further explore recent likelihood formulation signed utilizing Skellam distribution account weighted extend HM-LDM (sHM-LDM). Importantly, induced function explicitly attracts nodes positive links deters from having negative interactions. demonstrate utility sHM-LDM on several real networks. find that procedures successfully identify distinct structures, well relate extracted aspects favorable performances in terms prediction when compared baselines. Furthermore, learned soft enable easily interpretable visualizations highlighting patterns.

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ژورنال

عنوان ژورنال: Advances in Complex Systems

سال: 2023

ISSN: ['1793-6802', '1793-9615', '1793-9623', '0219-5259']

DOI: https://doi.org/10.1142/s0219525923400027