2 citations · 2 across the 2 of their papers we have counts for
4 papers
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning
Xi Chen, Yun Xiong, Siwei Zhang +7
Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from bo…
On provable privacy vulnerabilities of graph representations
Ruofan Wu, Guanhua Fang, Qiying Pan +3
Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabiliti…
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Jintang Li, Jiawang Dan, Ruofan Wu +9
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications,…
Privacy-preserving design of graph neural networks with applications to vertical federated learning
Ruofan Wu, Mingyang Zhang, Lingjuan Lyu +6
The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, h…