13 citations · 14 across the 5 of their papers we have counts for
5 papers
Federated Graph Learning with Structure Proxy Alignment
Xingbo Fu, Zihan Chen, Binchi Zhang +2
Federated Graph Learning (FGL) aims to learn graph learning models over graph data distributed in multiple data owners, which has been applied in various applications such as socia…
Understanding and Modeling Job Marketplace with Pretrained Language Models
Yaochen Zhu, Liang Wu, Binchi Zhang +5
Job marketplace is a heterogeneous graph composed of interactions among members (job-seekers), companies, and jobs. Understanding and modeling job marketplace can benefit both job…
IDEA: A Flexible Framework of Certified Unlearning for Graph Neural Networks
Yushun Dong, Binchi Zhang, Zhenyu Lei +2
Graph Neural Networks (GNNs) have been increasingly deployed in a plethora of applications. However, the graph data used for training may contain sensitive personal information of…
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach
Shujie Yang, Binchi Zhang, Shangbin Feng +4
Graph anomaly detection on attributed networks has become a prevalent research topic due to its broad applications in many influential domains. In real-world scenarios, nodes and e…
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications
Xingbo Fu, Binchi Zhang, Yushun Dong +2
Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are train…