most citedA Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions

6 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Negative Advantages Is a Double-Edged Sword: Calibrating advantages in GRPO for Search Agents

Jiayi Wu, Ruobing Xie, Zeqian Huang +6

Search agents achieve strong question-answering performance through multi-turn interactions with search engines, with Group Relative Policy Optimization (GRPO) being a widely used…

cs.AI2026

Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models

Can Xu, Lingyong Yan, Jiayi Wu +6

Recent advances in synergizing large reasoning models (LRMs) with retrieval-augmented generation (RAG) have shown promising results, yet two critical challenges remain: (1) reasoni…

cs.CL2024★ 1 cited

Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph

Yibo Zhao, Jiapeng Zhu, Can Xu +2

The rapid growth of social media platforms has raised significant concerns regarding online content toxicity. When Large Language Models (LLMs) are used for toxicity detection, two…

cs.LG2024★ 1 cited

Improving Graph Out-of-distribution Generalization Beyond Causality

Can Xu, Yao Cheng, Jianxiang Yu +4

Existing methods for graph out-of-distribution (OOD) generalization primarily rely on empirical studies on synthetic datasets. Such approaches tend to overemphasize the causal rela…

cs.SI2024★ 6 cited

A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions

Chenghua Gong, Yao Cheng, Jianxiang Yu +4

Graphs are structured data that models complex relations between real-world entities. Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar fe…