Instance Discrimination for Link Prediction

arXiv cs.LG Papers
instance-discrimination link-prediction self-supervised-learning graph-neural-networks graph-augmentation

Summary

This paper adapts instance discrimination self-supervised learning to link prediction in graphs, proposing new models L-GRACE and L-BGRL that operate on link representations and improve performance especially on unattributed graphs.

arXiv:2605.20257v1 Announce Type: new 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.
Original Article
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# Instance Discrimination for Link Prediction
Source: [https://arxiv.org/abs/2605.20257](https://arxiv.org/abs/2605.20257)
Authors:[Valentin Cuzin\-Rambaud](https://arxiv.org/search/cs?searchtype=author&query=Cuzin-Rambaud,+V)\(SyCoSMA, DM2L, LIRIS, UCBL\),[Mathieu Lefort](https://arxiv.org/search/cs?searchtype=author&query=Lefort,+M)\(LIRIS, SyCoSMA, IRISA, MALT, UR\),[Rémy Cazabet](https://arxiv.org/search/cs?searchtype=author&query=Cazabet,+R)\(DM2L, LIRIS, UCBL, IXXI\)

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> 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\.

## Submission history

From: Valentin Cuzin\-Rambaud \[[view email](https://arxiv.org/show-email/0996fe63/2605.20257)\] \[via CCSD proxy\] **\[v1\]**Mon, 18 May 2026 13:27:41 UTC \(350 KB\)

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