19 citations · 28 across the 5 of their papers we have counts for
5 papers
ADEdgeDrop: Adversarial Edge Dropping for Robust Graph Neural Networks
Zhaoliang Chen, Zhihao Wu, Ylli Sadikaj +5
Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the…
Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs
Zhaoliang Chen, Zhihao Wu, Luying Zhong +3
Heterogeneous graph neural networks aim to discover discriminative node embeddings and relations from multi-relational networks.One challenge of heterogeneous graph learning is the…
AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing
Zhaoliang Chen, Zhihao Wu, Zhenghong Lin +3
Graph Convolutional Network (GCN) with the powerful capacity to explore graph-structural data has gained noticeable success in recent years. Nonetheless, most of the existing GCN-b…
Multi-view Graph Convolutional Networks with Differentiable Node Selection
Zhaoliang Chen, Lele Fu, Shunxin Xiao +3
Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most…
Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning
Zhaoliang Chen, Lele Fu, Jie Yao +3
In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms. However, given multi-view da…