13 citations · 39 across the 18 of their papers we have counts for
7 papers · 1 filter
Federated Graph Learning with Structure Proxy Alignment
Xingbo Fu, Zihan Chen, Binchi Zhang +2
Federated Graph Learning (FGL) aims to learn graph learning models over graph data distributed in multiple data owners, which has been applied in various applications such as socia…
STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphs
Jiawen Wang, Quan Chen, Deze Zeng +3
Many emerging user-facing services adopt Graph Neural Networks (GNNs) to improve serving accuracy. When the graph used by a GNN model changes, representations (embedding) of nodes…
A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection
Jing Ma, Chen Chen, Anil Vullikanti +4
Methicillin-resistant Staphylococcus aureus (MRSA) is a type of bacteria resistant to certain antibiotics, making it difficult to prevent MRSA infections. Among decades of efforts…
Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
Yuchen Liu, Chen Chen, Lingjuan Lyu +3
Federated learning has exhibited vulnerabilities to Byzantine attacks, where the Byzantine attackers can send arbitrary gradients to a central server to destroy the convergence and…
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications
Xingbo Fu, Binchi Zhang, Yushun Dong +2
Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are train…
Rethinking Reinforcement Learning based Logic Synthesis
Chao Wang, Chen Chen, Dong Li +1
Recently, reinforcement learning has been used to address logic synthesis by formulating the operator sequence optimization problem as a Markov decision process. However, through e…