@inproceedings{
zhuo2024unified,
title={Unified Graph Augmentations for Generalized Contrastive Learning on Graphs},
author={Jiaming Zhuo and Yintong Lu and Hui Ning and Kun Fu and Bingxin Niu and Dongxiao He and Chuan Wang and Yuanfang Guo and Zhen Wang and Xiaochun Cao and Liang Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=jgkKroLxeC}
}
In real-world scenarios, networks (graphs) and their tasks possess unique characteristics, requiring the development of a versatile graph augmentation (GA) to meet the varied demands of network analysis. Unfortunately, most Graph Contrastive Learning (GCL) frameworks are hampered by the specificity, complexity, and incompleteness of their GA techniques. Firstly, GAs designed for specific scenarios may compromise the universality of models if mishandled. Secondly, the process of identifying and generating optimal augmentations generally involves substantial computational overhead. Thirdly, the effectiveness of the GCL, even the learnable ones, is constrained by the finite selection of GAs available. To overcome the above limitations, this paper introduces a novel unified GA module dubbed UGA after reinterpreting the mechanism of GAs in GCLs from a message-passing perspective. Theoretically, this module is capable of unifying any explicit GAs, including node, edge, attribute, and subgraph augmentations. Based on the proposed UGA, a novel generalized GCL framework dubbed Graph cOntrastive UnifieD Augmentations (GOUDA) is proposed. It seamlessly integrates widely adopted contrastive losses and an introduced independence loss to fulfill the common requirements of consistency and diversity of augmentation across diverse scenarios. Evaluations across various datasets and tasks demonstrate the generality and efficiency of the proposed GOUDA over existing state-of-the-art GCLs.
Unified Graph Augmentations for Generalized Contrastive Learning on Graphs
Jiaming Zhuo Yintong Lu Hui Ning Kun Fu Bingxin Niu Dongxiao He Chuan Wang Yuanfang Guo Zhen Wang Xiaochun Cao Liang Yang