most citedBalancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias

8 citations · 13 across the 5 of their papers we have counts for

collaborators

6 papers

cs.IR2026

HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document

Xin He, Yili Wang, Wenqi Fan +4

Question answering (QA) over complex documents requires models to retrieve and integrate evidence distributed across distant document regions and modalities. Multimodal GraphRAG pr…

cs.LG20261 cited

Dual Mamba for Node-Specific Representation Learning: Tackling Over-Smoothing with Selective State Space Modeling

Xin He, Yili Wang, Yiwei Dai +1

Over-smoothing remains a fundamental challenge in deep Graph Neural Networks (GNNs), where repeated message passing causes node representations to become indistinguishable. While e…

cs.LG20264 cited

Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space

Xin He, Yili Wang, Wenqi Fan +4

Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which…

cs.LG2026

Graph Defense Diffusion Model

Xin He, Wenqi Fan, Yili Wang +4

Graph Neural Networks (GNNs) are highly vulnerable to adversarial attacks, which can greatly degrade their performance. Existing graph purification methods attempt to address this…

cs.IR2026

Automatic Self-supervised Learning for Social Recommendations

Xin He, Wenqi Fan, Mingchen Sun +2

In recent years, researchers have leveraged social relations to enhance recommendation performance. However, most existing social recommendation methods require carefully designed…

cs.SI20268 cited

Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias

Xin He, Wenqi Fan, Ruobing Wang +4

Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplif…