4 papers
LHGstore: An In-Memory Learned Graph Storage for Fast Updates and Analytics
Pengpeng Qiao, Zhiwei Zhang, Xinzhou Wang +3
Various real-world applications rely on in-memory dynamic graphs that must efficiently handle frequent updates while supporting low-latency analytics on evolving structures. Achiev…
Spattack: Subgroup Poisoning Attacks on Federated Recommender Systems
Bo Yan, Yurong Hao, Dingqi Liu +5
Federated recommender systems (FedRec) have emerged as a promising approach to provide personalized recommendations while protecting user privacy. However, recent studies have show…
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…
Federated Graph Condensation with Information Bottleneck Principles
Bo Yan, Sihao He, Cheng Yang +3
Graph condensation (GC), which reduces the size of a large-scale graph by synthesizing a small-scale condensed graph as its substitution, has benefited various graph learning tasks…