87 citations · 305 across the 34 of their papers we have counts for
6 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…
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-…
Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network
Yuqing Hu, Xiaoyuan Cheng, Suhang Wang +3
The world is increasingly urbanizing and the building industry accounts for more than 40% of energy consumption in the United States. To improve urban sustainability, many cities a…
Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features
Tianxiang Zhao, Enyan Dai, Kai Shu +1
Despite the rapid development and great success of machine learning models, extensive studies have exposed their disadvantage of inheriting latent discrimination and societal bias…