358 citations · 669 across the 35 of their papers we have counts for
10 papers · 1 filter
Label-Wise Graph Convolutional Network for Heterophilic Graphs
Enyan Dai, Shijie Zhou, Zhimeng Guo +1
Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications. However, most existing GNNs assume the graphs exhibit strong homophil…
Towards Self-Explainable Graph Neural Network
Enyan Dai, Suhang Wang
Graph Neural Networks (GNNs), which generalize the deep neural networks to graph-structured data, have achieved great success in modeling graphs. However, as an extension of deep l…
Jointly Attacking Graph Neural Network and its Explanations
Wenqi Fan, Wei Jin, Xiaorui Liu +7
Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Despite the great success, recent studies have shown that GNNs are highly vulnerable to adve…
Graph Routing between Capsules
Yang Li, Wei Zhao, Erik Cambria +2
Routing methods in capsule networks often learn a hierarchical relationship for capsules in successive layers, but the intra-relation between capsules in the same layer is less stu…
Labeled Data Generation with Inexact Supervision
Enyan Dai, Kai Shu, Yiwei Sun +1
The recent advanced deep learning techniques have shown the promising results in various domains such as computer vision and natural language processing. The success of deep neural…
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs
Enyan Dai, Charu Aggarwal, Suhang Wang
Graph Neural Networks (GNNs) have achieved promising results for semi-supervised learning tasks on graphs such as node classification. Despite the great success of GNNs, many real-…