Semi-Supervised Deep Learning for Multiplex Networks

• URL: http://arxiv.org/abs/2110.02038v1
• Date: Tue, 5 Oct 2021 13:37:43 GMT
• Title: Semi-Supervised Deep Learning for Multiplex Networks
• Authors: Anasua Mitra, Priyesh Vijayan, Ranbir Sanasam, Diganta Goswami, Srinivasan Parthasarathy, Balaraman Ravindran
• Abstract summary: Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations. We present a novel semi-supervised approach for structure-aware representation learning on multiplex networks.
• Score: 20.671777884219555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations, each relation representing a distinct layer. Such graphs are used to investigate many complex biological, social, and technological systems. In this work, we present a novel semi-supervised approach for structure-aware representation learning on multiplex networks. Our approach relies on maximizing the mutual information between local node-wise patch representations and label correlated structure-aware global graph representations to model the nodes and cluster structures jointly. Specifically, it leverages a novel cluster-aware, node-contextualized global graph summary generation strategy for effective joint-modeling of node and cluster representations across the layers of a multiplex network. Empirically, we demonstrate that the proposed architecture outperforms state-of-the-art methods in a range of tasks: classification, clustering, visualization, and similarity search on seven real-world multiplex networks for various experiment settings.

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