Search Results for author: Tianxiang Zhao

Found 6 papers, 1 papers with code

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

Exploring Edge Disentanglement for Node Classification

no code implementations23 Feb 2022 Tianxiang Zhao, Xiang Zhang, Suhang Wang

Concretely, these self-supervision tasks are enforced on a designed edge disentanglement module to be trained jointly with the downstream node classification task to encourage automatic edge disentanglement.

Classification Disentanglement +2

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

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

no code implementations29 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.


GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

2 code implementations16 Mar 2021 Tianxiang Zhao, Xiang Zhang, Suhang Wang

This task is non-trivial, as previous synthetic minority over-sampling algorithms fail to provide relation information for newly synthesized samples, which is vital for learning on graphs.

Classification General Classification +2

Semi-Supervised Graph-to-Graph Translation

no code implementations16 Mar 2021 Tianxiang Zhao, Xianfeng Tang, Xiang Zhang, Suhang Wang

For example, we can easily build graphs representing peoples' shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain.

Graph-To-Graph Translation Translation

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