Semi-Supervised Deep Learning for Multiplex Networks
This work addresses the problem of analyzing complex multiplex networks for researchers in fields like biology and social sciences, though it appears incremental as it builds on existing representation learning techniques.
The paper tackles representation learning on multiplex networks by proposing a semi-supervised approach that maximizes mutual information between local node and global graph representations, and it demonstrates superior performance over state-of-the-art methods in tasks like classification and clustering on seven real-world datasets.
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.