# TP-GCL: graph contrastive learning from the tensor perspective

## TL;DR

TP-GCL is a new graph contrastive learning method that improves performance on complex graph structures and sparse data using a tensor-based approach.

## Contribution

The novel TP-GCL method introduces a tensor perspective for graph contrastive learning, enhancing generalization and handling of complex structures.

## Key findings

- TP-GCL outperforms baseline methods on multiple public datasets.
- The method effectively captures complex structural information through hypergraphs and high-order tensors.
- TP-GCL shows enhanced generalization and effectiveness with sparse labeled data.

## Abstract

Graph Neural Networks (GNNs) have demonstrated significant potential as powerful tools for handling graph data in various fields. However, traditional GNNs often encounter limitations in information capture and generalization when dealing with complex and high-order graph structures. Concurrently, the sparse labeling phenomenon in graph data poses challenges in practical applications. To address these issues, we propose a novel graph contrastive learning method, TP-GCL, based on a tensor perspective. The objective is to overcome the limitations of traditional GNNs in modeling complex structures and addressing the issue of sparse labels. Firstly, we transform ordinary graphs into hypergraphs through clique expansion and employ high-order adjacency tensors to represent hypergraphs, aiming to comprehensively capture their complex structural information. Secondly, we introduce a contrastive learning framework, using the original graph as the anchor, to further explore the differences and similarities between the anchor graph and the tensorized hypergraph. This process effectively extracts crucial structural features from graph data. Experimental results demonstrate that TP-GCL achieves significant performance improvements compared to baseline methods across multiple public datasets, particularly showcasing enhanced generalization capabilities and effectiveness in handling complex graph structures and sparse labeled data.