3 papers
cs.LG2025
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…
cs.LG2025
Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement
Yinlin Zhu, Xunkai Li, Jishuo Jia +3
Recent advances in graph machine learning have shifted to data-centric paradigms, driven by two emerging fields: (1) Federated graph learning (FGL) enables multi-client collaborati…
cs.LG2024
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
Xunkai Li, Zhengyu Wu, Jiayi Wu +4
With the increasing prevalence of cross-domain Text-Attributed Graph (TAG) Data (e.g., citation networks, recommendation systems, social networks, and ai4science), the integration…