75 citations · 83 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Deep Efficient Private Neighbor Generation for Subgraph Federated Learning
Ke Zhang, Lichao Sun, Bolin Ding +2
Behemoth graphs are often fragmented and separately stored by multiple data owners as distributed subgraphs in many realistic applications. Without harming data privacy, it is natu…
cs.LG2021★ 75 cited
Subgraph Federated Learning with Missing Neighbor Generation
Ke Zhang, Carl Yang, Xiaoxiao Li +2
Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions. As graphs are getting bigger and b…
cs.SI2020★ 8 cited
Secure Deep Graph Generation with Link Differential Privacy
Carl Yang, Haonan Wang, Ke Zhang +2
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sen…