Tensor-Fused Multi-View Graph Contrastive Learning

TL;DR

TensorMV-GCL introduces a multi-view graph contrastive learning framework that fuses topological and graph features using tensor methods, improving graph classification performance and robustness on diverse datasets.

Contribution

It presents a novel tensor-based multi-view contrastive learning framework that integrates extended persistent homology for enhanced topological feature extraction in graphs.

Findings

Outperforms 15 state-of-the-art methods on 9 out of 11 benchmarks.

Effectively captures complex topological features with tensor fusion.

Reduces computational overhead via tensor aggregation and contraction.

Abstract

Graph contrastive learning (GCL) has emerged as a promising approach to enhance graph neural networks' (GNNs) ability to learn rich representations from unlabeled graph-structured data. However, current GCL models face challenges with computational demands and limited feature utilization, often relying only on basic graph properties like node degrees and edge attributes. This constrains their capacity to fully capture the complex topological characteristics of real-world phenomena represented by graphs. To address these limitations, we propose Tensor-Fused Multi-View Graph Contrastive Learning (TensorMV-GCL), a novel framework that integrates extended persistent homology (EPH) with GCL representations and facilitates multi-scale feature extraction. Our approach uniquely employs tensor aggregation and compression to fuse information from graph and topological features obtained from multiple augmented views of the same graph. By incorporating tensor concatenation and contraction modules, we reduce computational overhead by separating feature tensor aggregation and transformation. Furthermore, we enhance the quality of learned topological features and model robustness through noise-injected EPH. Experiments on molecular, bioinformatic, and social network datasets demonstrate TensorMV-GCL's superiority, outperforming 15 state-of-the-art methods in graph classification tasks across 9 out of 11 benchmarks while achieving comparable results on the remaining two. The code for this paper is publicly available at https://github.com/CS-SAIL/Tensor-MV-GCL.git.