63 citations · 92 across the 4 of their papers we have counts for
4 papers
DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
Qiuchen Zhang, Hong kyu Lee, Jing Ma +3
Graph Neural Networks (GNNs) have achieved great success in learning with graph-structured data. Privacy concerns have also been raised for the trained models which could expose th…
Temporal Network Embedding via Tensor Factorization
Jing Ma, Qiuchen Zhang, Jian Lou +2
Representation learning on static graph-structured data has shown a significant impact on many real-world applications. However, less attention has been paid to the evolving nature…
Federated Graph Classification over Non-IID Graphs
Han Xie, Jing Ma, Li Xiong +1
Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can al…
Spatio-Temporal Tensor Sketching via Adaptive Sampling
Jing Ma, Qiuchen Zhang, Joyce C. Ho +1
Mining massive spatio-temporal data can help a variety of real-world applications such as city capacity planning, event management, and social network analysis. The tensor represen…