不依赖数据增强，通过边特征对比提升图表示学习效果。
Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning Model
- 用节点嵌入拼接生成边特征，无需数据增强
- 相同节点连接的边为正例，其余为负例，实现边间对比
- 在标签极少场景下超越主流自监督与部分有监督模型
图对比学习（GCL）旨在自监督地从无标签图数据中学习表示，近年来发展迅速。然而，现有方法大多未充分探索边级别的对比。多数研究仅将边视为更新节点特征的辅助信息。边级GCL的主要障碍是计算开销大。为此，我们提出一种高效学习边特征的模型——无增强边对比学习（AFECL），实现边-边对比。AFECL不依赖任何数据增强，包含两部分：首先设计新型边特征生成方法，通过连接节点的嵌入拼接计算边特征；其次构建边对比学习机制，将连接相同节点的边定义为正样本对，其他边为负样本对。实验表明，相比近期最先进GCL方法甚至部分有监督GNN，AFECL在链接预测和极少量标签下的半监督节点分类任务中达到最先进性能。代码已开源：https://github.com/YujunLi361/AFECL。
原文摘要 · Abstract (English)
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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