3 papers
cs.CL2026
Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning
Zhongjian Zhang, Xiao Wang, Mengmei Zhang +2
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph-related tasks, with the ultimate goal of de…
cs.LG2025
Data-centric Federated Graph Learning with Large Language Models
Bo Yan, Zhongjian Zhang, Huabin Sun +3
In federated graph learning (FGL), a complete graph is divided into multiple subgraphs stored in each client due to privacy concerns, and all clients jointly train a global graph m…
cs.CR2025
Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective
Zhongjian Zhang, Mengmei Zhang, Xiao Wang +4
To preserve user privacy in recommender systems, federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating…