Search Results for author: Enyan Dai

Found 18 papers, 10 papers with code

Unnoticeable Backdoor Attacks on Graph Neural Networks

1 code implementation11 Feb 2023 Enyan Dai, Minhua Lin, Xiang Zhang, Suhang Wang

In particular, backdoor attack poisons the graph by attaching triggers and the target class label to a set of nodes in the training graph.

Backdoor Attack Graph Classification +1

HP-GMN: Graph Memory Networks for Heterophilous Graphs

1 code implementation15 Oct 2022 Junjie Xu, Enyan Dai, Xiang Zhang, Suhang Wang

Graph neural networks (GNNs) have achieved great success in various graph problems.

Towards Prototype-Based Self-Explainable Graph Neural Network

no code implementations5 Oct 2022 Enyan Dai, Suhang Wang

Therefore, we study a novel problem of learning prototype-based self-explainable GNNs that can simultaneously give accurate predictions and prototype-based explanations on predictions.

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

no code implementations18 Apr 2022 Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, Suhang Wang

Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society.

Drug Discovery Fairness

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

4 code implementations ICLR 2022 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 and traffic networks.

Density Estimation Time Series +2

Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels

1 code implementation1 Jan 2022 Enyan Dai, Wei Jin, Hui Liu, Suhang Wang

To mitigate these issues, we propose a novel framework which adopts the noisy edges as supervision to learn a denoised and dense graph, which can down-weight or eliminate noisy edges and facilitate message passing of GNNs to alleviate the issue of limited labeled nodes.

Label-Wise Graph Convolutional Network for Heterophilic Graphs

1 code implementation15 Oct 2021 Enyan Dai, Shijie Zhou, Zhimeng Guo, Suhang Wang

Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications.

Node Classification Representation Learning

Towards Self-Explainable Graph Neural Network

1 code implementation26 Aug 2021 Enyan Dai, Suhang Wang

Though many efforts are taken to improve the explainability of deep learning, they mainly focus on i. i. d data, which cannot be directly applied to explain the predictions of GNNs because GNNs utilize both node features and graph topology to make predictions.

Node Classification

Labeled Data Generation with Inexact Supervision

no code implementations8 Jun 2021 Enyan Dai, Kai Shu, Yiwei Sun, Suhang Wang

We propose a novel generative framework named as ADDES which can synthesize high-quality labeled data for target classification tasks by learning from data with inexact supervision and the relations between inexact supervision and target classes.


NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs

1 code implementation8 Jun 2021 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.

Node Classification

Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network

no code implementations27 May 2021 Yuqing Hu, Xiaoyuan Cheng, Suhang Wang, Jianli Chen, Tianxiang Zhao, Enyan Dai

After discussion, it is found that data-driven models integrated engineering or physical knowledge can significantly improve the urban building energy simulation.

Time Series Analysis

Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features

1 code implementation29 Apr 2021 Tianxiang Zhao, Enyan Dai, Kai Shu, Suhang Wang

Though the sensitive attribute of each data sample is unknown, we observe that there are usually some non-sensitive features in the training data that are highly correlated with sensitive attributes, which can be used to alleviate the bias.

BIG-bench Machine Learning Fairness

Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information

1 code implementation3 Sep 2020 Enyan Dai, Suhang Wang

Though extensive studies of fair classification have been conducted on i. i. d data, methods to address the problem of discrimination on non-i. i. d data are rather limited.

Fairness General Classification +1

Unsupervised Image Super-Resolution with an Indirect Supervised Path

no code implementations7 Oct 2019 Zhen Han, Enyan Dai, Xu Jia, Xiaoying Ren, Shuaijun Chen, Chunjing Xu, Jianzhuang Liu, Qi Tian

The task of single image super-resolution (SISR) aims at reconstructing a high-resolution (HR) image from a low-resolution (LR) image.

Image Super-Resolution Translation

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