3 papers
cs.LG2024
OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
Xunkai Li, Yinlin Zhu, Boyang Pang +7
Federated graph learning (FGL) is a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach inher…
cs.CR2024
K-stars LDP: A Novel Framework for (p, q)-clique Enumeration under Local Differential Privacy
Henan Sun, Zhengyu Wu, Rong-Hua Li +2
(p,q)-clique enumeration on a bipartite graph is critical for calculating clustering coefficient and detecting densest subgraph. It is necessary to carry out subgraph enumeration w…
cs.CR2023
Privacy-Preserving Graph Embedding based on Local Differential Privacy
Zening Li, Rong-Hua Li, Meihao Liao +2
Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks,…