Semi-Supervised Deep Learning for Multiplex Networks

TL;DR

This paper introduces a semi-supervised deep learning method for multiplex networks that enhances node and cluster representation learning by maximizing mutual information, improving performance in classification, clustering, and visualization tasks.

Contribution

It presents a novel structure-aware semi-supervised approach that jointly models nodes and clusters across multiplex network layers using mutual information maximization.

Findings

Outperforms state-of-the-art methods in classification tasks

Achieves superior clustering and visualization results

Effective across seven real-world multiplex networks

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.