Instance Discrimination for Link Prediction
The paper titled 'Instance Discrimination for Link Prediction' explores the application of instance discrimination models in the context of link prediction within graphs. The authors propose new models that enhance performance, particularly on unattributed graphs, and demonstrate their effectiveness through rigorous evaluation. This research contributes to the growing field of self-supervised learning in machine learning.
- ▪Instance discrimination models have shown promise in self-supervised learning for both images and graphs.
- ▪The authors introduce two new models, L-GRACE and L-BGRL, which focus on link representations.
- ▪The study highlights the importance of the augmentation process in achieving better performance for link prediction.
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Computer Science > Machine Learning arXiv:2605.20257 (cs) [Submitted on 18 May 2026] Title:Instance Discrimination for Link Prediction Authors:Valentin Cuzin-Rambaud (SyCoSMA, DM2L, LIRIS, UCBL), Mathieu Lefort (LIRIS, SyCoSMA, IRISA, MALT, UR), Rémy Cazabet (DM2L, LIRIS, UCBL, IXXI) View a PDF of the paper titled Instance Discrimination for Link Prediction, by Valentin Cuzin-Rambaud (SyCoSMA and 12 other authors View PDF 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.
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