1.2k citations · 4.2k across the 61 of their papers we have counts for
4 papers · 1 filter
Understanding Structural Vulnerability in Graph Convolutional Networks
Liang Chen, Jintang Li, Qibiao Peng +3
Recent studies have shown that Graph Convolutional Networks (GCNs) are vulnerable to adversarial attacks on the graph structure. Although multiple works have been proposed to impro…
A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee
Chunjiang Che, Xiaoli Li, Chuan Chen +2
Federated learning allows multiple participants to collaboratively train an efficient model without exposing data privacy. However, this distributed machine learning training metho…
FedGL: Federated Graph Learning Framework with Global Self-Supervision
Chuan Chen, Weibo Hu, Ziyue Xu +1
Graph data are ubiquitous in the real world. Graph learning (GL) tries to mine and analyze graph data so that valuable information can be discovered. Existing GL methods are design…
GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software
Jintang Li, Kun Xu, Liang Chen +2
Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data. So far GNNs is becoming an increasingly critical role in software e…