Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networks

• URL: http://arxiv.org/abs/2504.16748v2
• Date: Thu, 24 Apr 2025 14:26:02 GMT
• Title: Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
• Authors: Yanan Zhao, Feng Ji, Kai Zhao, Xuhao Li, Qiyu Kang, Wenfei Liang, Yahya Alkhatib, Xingchao Jian, Wee Peng Tay,
• Abstract summary: Graph Contrastive Learning (GCL) has recently made progress as an unsupervised graph representation learning paradigm.<n>We introduce a novel augmentation-free GCL framework based on graph neural diffusion models.<n>We demonstrate that our model does not require negative samples for training and is applicable to both homophilic and heterophilic datasets.
• Score: 24.778889911467438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Contrastive Learning (GCL) has recently made progress as an unsupervised graph representation learning paradigm. GCL approaches can be categorized into augmentation-based and augmentation-free methods. The former relies on complex data augmentations, while the latter depends on encoders that can generate distinct views of the same input. Both approaches may require negative samples for training. In this paper, we introduce a novel augmentation-free GCL framework based on graph neural diffusion models. Specifically, we utilize learnable encoders governed by Fractional Differential Equations (FDE). Each FDE is characterized by an order parameter of the differential operator. We demonstrate that varying these parameters allows us to produce learnable encoders that generate diverse views, capturing either local or global information, for contrastive learning. Our model does not require negative samples for training and is applicable to both homophilic and heterophilic datasets. We demonstrate its effectiveness across various datasets, achieving state-of-the-art performance.

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