Haipeng Yang — accepted AI-conference papers

2026

GCIB: Graph Contrastive Information Bottleneck for Multi-Behavior Recommendation

Likang Wu, Zihao Chen, Jianxin Zhang, Sangqi Zhu, Yuanyuan Ge, Haipeng Yang +1

ICML 2026poster

With the rapid emergence of multi-behavior learning in recommender systems, leveraging auxiliary user behaviors has proven effective for mitigating target-behavior data sparsity. Yet auxiliary behavior graphs frequently contain noisy or irrelevant interactions that do not align with the target task,…

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2026

Robust Contrastive Graph Clustering with Adaptive Local-Global Integration

Lei Zhang, Fubo Sun, Haipeng Yang, Zhong Guan, Likang Wu

IJCAI 2026

Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contrastive learning that have improved clustering via structural and attribute signals, existing methods still struggle to flexibly capture high-order l

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