1 citations · 1 across the 5 of their papers we have counts for
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FedBook: A Unified Federated Graph Foundation Codebook with Intra-domain and Inter-domain Knowledge Modeling
Zhengyu Wu, Yinlin Zhu, Xunkai Li +4
Foundation models have shown remarkable cross-domain generalization in language and vision, inspiring the development of graph foundation models (GFMs). However, existing GFMs typi…
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives
Zhengyu Wu, Boyang Pang, Xunkai Li +6
Federated Graph Learning (FGL) enables privacy-preserving, distributed training of graph neural networks without sharing raw data. Among its approaches, subgraph-FL has become the…
Federated Prototype Graph Learning
Zhengyu Wu, Xunkai Li, Yinlin Zhu +3
In recent years, Federated Graph Learning (FGL) has gained significant attention for its distributed training capabilities in graph-based machine intelligence applications, mitigat…
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
Zhengyu Wu, Guang Zeng, Huilin Lai +7
Federated graph learning (FGL) has emerged as a promising paradigm for collaborative graph representation learning, enabling multiple parties to jointly train models while preservi…
Chemistry-Inspired Diffusion with Non-Differentiable Guidance
Yuchen Shen, Chenhao Zhang, Sijie Fu +3
Recent advances in diffusion models have shown remarkable potential in the conditional generation of novel molecules. These models can be guided in two ways: (i) explicitly, throug…