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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20257 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | GfFn7vtTRVdC |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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