91 citations · 128 across the 9 of their papers we have counts for
5 papers · 1 filter
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Bingzhe Wu, Jintang Li, Junchi Yu +17
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite t…
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
Jamie Cui, Chaochao Chen, Lingjuan Lyu +2
Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that a…
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
Chaochao Chen, Huiwen Wu, Jiajie Su +3
Cross Domain Recommendation (CDR) has been popularly studied to alleviate the cold-start and data sparsity problem commonly existed in recommender systems. CDR models can improve t…
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty
Yilun Lin, Chaochao Chen, Cen Chen +1
Federated learning (FL) has attracted increasing attention in recent years. As a privacy-preserving collaborative learning paradigm, it enables a broader range of applications, esp…
ASFGNN: Automated Separated-Federated Graph Neural Network
Longfei Zheng, Jun Zhou, Chaochao Chen +3
Graph Neural Networks (GNNs) have achieved remarkable performance by taking advantage of graph data. The success of GNN models always depends on rich features and adjacent relation…