Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model

• URL: http://arxiv.org/abs/2412.11075v1
• Date: Sun, 15 Dec 2024 06:16:01 GMT
• Title: Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model
• Authors: Yujun Li, Hongyuan Zhang, Yuan Yuan,
• Abstract summary: Graph contrastive learning (GCL) aims to learn representations from unlabeled graph data in a self-supervised manner. One of the primary obstacles of edge-based GCL is the heavy burden. We propose AugmentationFree Edge Contrastive Learning (AFECL) to achieve edgeedge contrast.
• Score: 18.02317423788033
• License:
• Abstract: Graph contrastive learning (GCL) aims to learn representations from unlabeled graph data in a self-supervised manner and has developed rapidly in recent years. However, edgelevel contrasts are not well explored by most existing GCL methods. Most studies in GCL only regard edges as auxiliary information while updating node features. One of the primary obstacles of edge-based GCL is the heavy computation burden. To tackle this issue, we propose a model that can efficiently learn edge features for GCL, namely AugmentationFree Edge Contrastive Learning (AFECL) to achieve edgeedge contrast. AFECL depends on no augmentation consisting of two parts. Firstly, we design a novel edge feature generation method, where edge features are computed by embedding concatenation of their connected nodes. Secondly, an edge contrastive learning scheme is developed, where edges connecting the same nodes are defined as positive pairs, and other edges are defined as negative pairs. Experimental results show that compared with recent state-of-the-art GCL methods or even some supervised GNNs, AFECL achieves SOTA performance on link prediction and semi-supervised node classification of extremely scarce labels. The source code is available at https://github.com/YujunLi361/AFECL.

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