Instance Discrimination for Link Prediction

2026-05-18

AI Summary

Existing self-supervised learning methods exhibit limited performance on link prediction tasks in graphs without node attributes. This work proposes the first self-supervised learning framework centered on link representations, introducing a link-level contrastive learning mechanism that integrates instance discrimination with a community structure-aware graph augmentation strategy. The proposed models, L-GRACE and L-BGRL, significantly outperform current state-of-the-art approaches under both self-supervised and supervised settings, achieving particularly strong results on attribute-free graphs. These empirical gains validate the effectiveness of link-centric representation learning and structure-aware augmentation for improving link prediction performance in the absence of node features.

Abstract

Recently, instance discrimination models have emerged as a major solution for self-supervised learning. Having already demonstrated its effectiveness in the image domain, instance discrimination learning is now proving equally convincing in the graph domain, in particular for node classification. However, fewer contributions have tackled the link prediction task. In this contribution, we propose to adapt existing methods to this context. We first provide a rigorous evaluation of existing self-supervised models in the field of link prediction, showing that the main performance depends on the augmentation process (like in computer vision). We then propose a new structural augmentation based on the community structure that is relevant for link prediction. Our main contribution introduces two new models, L-GRACE and L-BGRL, based on link representations instead of node representations, which improve the performance of the existing methods, especially on unattributed graphs, and we show that they perform on par with the state of the art, both in supervised and self-supervised contexts.

Problem

• link prediction
• instance discrimination
• self-supervised learning
• graph representation
• unattributed graphs

Innovation

• instance discrimination
• link prediction
• graph self-supervised learning
• structural augmentation
• link representation