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20202026
most citedA Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

87 citations · 305 across the 34 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.LG2026

TriGlue: a Biology-Inspired Generative Model for Generating Molecular Glue-Induced Ternary Complex

Yuliang Yan, Shuo Yan, Haochun Tang +2

Molecular glue degraders have emerged as a promising strategy for targeted protein degradation by inducing ternary complex formation between an E3 ubiquitin ligase and a target pro…

cs.LG2026

Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks

Yuxiang Zhang, Bin Ma, Enyan Dai

Graph Neural Networks (GNNs) have achieved remarkable results in various tasks. Recent studies reveal that graph backdoor attacks can poison the GNN model to predict test nodes wit…

cs.LG2026

SCL-GNN: Towards Generalizable Graph Neural Networks via Spurious Correlation Learning

Yuxiang Zhang, Enyan Dai

Graph Neural Networks (GNNs) have demonstrated remarkable success across diverse tasks. However, their generalization capability is often hindered by spurious correlations between…

cs.AI2026

How Far Are LLMs from Professional Poker Players? Revisiting Game-Theoretic Reasoning with Agentic Tool Use

Minhua Lin, Enyan Dai, Hui Liu +11

As Large Language Models (LLMs) are increasingly applied in high-stakes domains, their ability to reason strategically under uncertainty becomes critical. Poker provides a rigorous…

cs.SI2026

FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks

Renqiang Luo, Huafei Huang, Tao Tang +5

Graph Transformers (GTs) are increasingly applied to social network analysis, yet their deployment is often constrained by fairness concerns. This issue is particularly critical in…