Lingren Wang

2 accepted papers

2026

Anchor-Driven Nyström for Deep Graph-Level Clustering

Jiaxin Wang, Wenxuan Tu, Lingren Wang, Jieren Cheng, Yue Yang

AAAI 2026technical

Graph-level clustering (GLC), which aims to group entire graphs according to their structural and attribute-based similarities, represents a fundamental yet challenging task in various practical applications. Existing GLC methods primarily fall into two main paradigms: 1) deep graph clustering appro

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2025

FedIGL: Federated Invariant Graph Learning for Non-IID Graphs

Lingren Wang, Wenxuan Tu, Jiaxin Wang, Xiong Wang, Jieren Cheng, Jingxin Liu

NeurIPS 2025poster

Federated Graph Learning (FGL) shows superiority in cross-domain graph training while preserving data privacy. Existing approaches usually assume shared generic knowledge (e.g., prototypes, spectral features) via aggregating local structures statistically to alleviate structural heterogeneity. Howev…

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