Deep node clustering based on mutual information maximization

[摘要] Variational Graph Autoencoders (VGAs) are generative models for unsupervised learning of node representations within graph data. While VGAs have been achieved state-of-the-art results for different predictive tasks on graph-structured data, they are susceptible to the over-pruning problem where only a small subset of the stochastic latent units are active. This can limit their modeling capacity and their ability to learn meaningful representations. In this paper, we present SOLI (Stacked auto-encoder for nOde cLusterIng), an information maximization approach for learning graph representations by leveraging maximal cliques. SOLI relies on aggregating useful representations by assigning clique-based weights to various edges in a neighborhood while maximizing mutual information. The learned representations are mindful of graph patches centered around each node, and can be used for a range of downstream tasks, and thus encouraging more active units. We demonstrate strong performance across three graph benchmark datasets.(Code is available at https://github.com/SoheilaMolaei/SOLI.) (c) 2021 Elsevier B.V. All rights reserved.

[发布日期] 2021-09-30

[关键词] Graph representation learning;Graph neural network;Graph auto-encoder;Node clustering;Link prediction