Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization

TL;DR

This paper introduces an unsupervised hierarchical graph representation learning method that maximizes mutual information to capture higher-order structural features, improving interpretability and performance on classification tasks.

Contribution

It proposes UHGR, a novel unsupervised method for hierarchical graph representations that leverages mutual information maximization, addressing limitations of existing GNNs and supervised approaches.

Findings

Achieves comparable performance to supervised methods on benchmarks.

Captures meaningful and interpretable hierarchical clusters.

Enables unsupervised node and graph classification.

Abstract

Graph representation learning based on graph neural networks (GNNs) can greatly improve the performance of downstream tasks, such as node and graph classification. However, the general GNN models do not aggregate node information in a hierarchical manner, and can miss key higher-order structural features of many graphs. The hierarchical aggregation also enables the graph representations to be explainable. In addition, supervised graph representation learning requires labeled data, which is expensive and error-prone. To address these issues, we present an unsupervised graph representation learning method, Unsupervised Hierarchical Graph Representation (UHGR), which can generate hierarchical representations of graphs. Our method focuses on maximizing mutual information between "local" and high-level "global" representations, which enables us to learn the node embeddings and graph embeddings without any labeled data. To demonstrate the effectiveness of the proposed method, we perform the node and graph classification using the learned node and graph embeddings. The results show that the proposed method achieves comparable results to state-of-the-art supervised methods on several benchmarks. In addition, our visualization of hierarchical representations indicates that our method can capture meaningful and interpretable clusters.