Paper

Geometrical fairness in graph neural networks

Unreadunread

AI summary

arXiv:2606. 17684v1 Announce Type: new Abstract: Graph-based learning methods have become increasingly prominent due to their strong performance across diverse applications.

Reading status

Priority

About this paper

Authors: Arturo P\'erez-Peralta, Sandra Ben\'itez-Pe\~na, Blas Kolic, Rosa E. Lillo
Released: 2026-06-17 (22d ago)
Topic: Related to current project
Relation to my work: Read next because Geometrical fairness in graph neural networks overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, under, eval, rate, project, propagate, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.17684

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Geometrical fairness in graph neural networks. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.