Asymmetric Graph Contrastive Learning

Author:

Chang Xinglong12,Wang Jianrong13,Guo Rui3,Wang Yingkui4,Li Weihao5

Affiliation:

1. School of New Media and Communication, Tianjin University, Tianjin 300350, China

2. Qijia Youdao Network Technology (Beijing) Co., Ltd., Beijing 100012, China

3. College of Intelligence and Computing, Tianjin University, Tianjin 300350, China

4. Department of Computer Science and Technology, Tianjin Renai College, Tianjin 301636, China

5. Data61-CSIRO, Black Mountain Laboratories, Canberra, ACT 2601, Australia

Abstract

Learning effective graph representations in an unsupervised manner is a popular research topic in graph data analysis. Recently, contrastive learning has shown its success in unsupervised graph representation learning. However, how to avoid collapsing solutions for contrastive learning methods remains a critical challenge. In this paper, a simple method is proposed to solve this problem for graph representation learning, which is different from existing commonly used techniques (such as negative samples or predictor network). The proposed model mainly relies on an asymmetric design that consists of two graph neural networks (GNNs) with unequal depth layers to learn node representations from two augmented views and defines contrastive loss only based on positive sample pairs. The simple method has lower computational and memory complexity than existing methods. Furthermore, a theoretical analysis proves that the asymmetric design avoids collapsing solutions when training together with a stop-gradient operation. Our method is compared to nine state-of-the-art methods on six real-world datasets to demonstrate its validity and superiority. The ablation experiments further validated the essential role of the asymmetric architecture.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

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