18 citations · 30 across the 8 of their papers we have counts for
10 papers
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Enyan Dai, Limeng Cui, Zhengyang Wang +5
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured data. However, recent works show that GNNs are vulnerable to adversarial attacks which can foo…
HP-GMN: Graph Memory Networks for Heterophilous Graphs
Junjie Xu, Enyan Dai, Xiang Zhang +1
Graph neural networks (GNNs) have achieved great success in various graph problems. However, most GNNs are Message Passing Neural Networks (MPNNs) based on the homophily assumption…
Towards Prototype-Based Self-Explainable Graph Neural Network
Enyan Dai, Suhang Wang
Graph Neural Networks (GNNs) have shown great ability in modeling graph-structured data for various domains. However, GNNs are known as black-box models that lack interpretability.…
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
Enyan Dai, Jie Chen
Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids…
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…
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…