Zhimeng Guo

Brief Bio

I am a graduate student at Penn State's College of Information Sciences and Technology (IST). I work on the challenges associated with causality, robustness and graph. For example, spurious correlation, model explanation and out of distribution problems.

2021 - Present: Graduate student at Penn State. I work on graph neural networks and causality.
2017 - 2021: B.Sc. at UESTC, advised by Prof. Lianli Gao and Prof. Linyuan Lv.

Publications

Towards Fair Graph Neural Networks via Graph Counterfactual.
Zhimeng Guo, Jialiang Li, Teng Xiao, Yao Ma, and Suhang Wang. CIKM, 2023.
[bib] [paper] [code]

@article{guo2023towards,
  title={Towards Fair Graph Neural Networks via Graph Counterfactual},
  author={Guo, Zhimeng and Li, Jialiang and Xiao, Teng and Ma, Yao and Wang, Suhang},
  journal={arXiv preprint arXiv:2307.04937},
  year={2023}
}

Counterfactual Learning on Graphs: A Survey.
Zhimeng Guo, Teng Xiao, Charu Aggarwal, Hui Liu, and Suhang Wang. arXiv, 2023.
[bib] [paper] [code]

@misc{guo2023counterfactual,
  title={Counterfactual Learning on Graphs: A Survey}, 
  author={Zhimeng Guo and Teng Xiao and Charu Aggarwal and Hui Liu and Suhang Wang},
  year={2023},
  eprint={2304.01391},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

Fairness-aware Message Passing for Graph Neural Networks.
Huaisheng Zhu, Guoji Fu, Zhimeng Guo, Zhiwei Zhang, Teng Xiao, and Suhang Wang. arXiv, 2023.
[bib] [paper]

@article{zhu2023fairnessaware,
  author = {Zhu, Huaisheng and Fu, Guoji and Guo, Zhimeng and Zhang, Zhiwei and Xiao, Teng and Wang, Suhang},
  journal = {arXiv},
  year = {2023},
  title = {Fairness-aware Message Passing for Graph Neural Networks},
  volume = {abs/2306.11132},
}

Decoupled Self-supervised Learning for Graphs.
Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, and Suhang Wang. NeurIPS, 2022.
[bib] [paper]

@inproceedings{xiao2022decoupled,
  author = {Xiao, Teng and Chen, Zhengyu and Guo, Zhimeng and Zhuang, Zeyang and Wang, Suhang},
  booktitle = {Conference on Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  pages = {620--634},
  title = {Decoupled Self-supervised Learning for Graphs},
  volume = {35},
}

Label-Wise Graph Convolutional Network for Heterophilic Graphs.
Enyan Dai, Shijie Zhou, Zhimeng Guo, and Suhang Wang. LoG, 2022.
[bib] [paper] [code]

@article{dai2022comprehensive,
  title={A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability},
  author={Dai, Enyan and Zhao, Tianxiang and Zhu, Huaisheng and Xu, Junjie and Guo, Zhimeng and Liu, Hui and Tang, Jiliang and Wang, Suhang},
  journal={arXiv preprint arXiv:2204.08570},
  year={2022}
}

Link Prediction on Heterophilic Graphs via Disentangled Representation Learning.
Shijie Zhou, Zhimeng Guo, Charu Aggarwal, Xiang Zhang, and Suhang Wang. arXiv, 2022.
[bib] [paper] [code]

@article{zhou2022link,
  author = {Zhou, Shijie and Guo, Zhimeng and Aggarwal, C. and Zhang, Xiang and Wang, Suhang},
  journal = {arXiv},
  year = {2022},
  title = {Link Prediction on Heterophilic Graphs via Disentangled Representation Learning},
  volume = {abs/2208.01820},
}

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability.
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. arXiv, 2022.
[bib] [paper] [code]

@article{dai2022a,
  author = {Dai, Enyan and Zhao, Tianxiang and Zhu, Huaisheng and Xu, Junjie and Guo, Zhimeng and Liu, Hui and Tang, Jiliang and Wang, Suhang},
  journal = {arXiv},
  year = {2022},
  title = {A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability},
  volume = {abs/2204.08570},
}

Teaching

IST 597 Data Science for Researchers and Scholars, Fall 2023.

Awards / Acknowledgements / Services

Reviewer and subreviewer for AISTATS, NeurIPS, ICLR, SIGIR, IJCAI.
Outstanding Undergraduate Thesis Award.
Ranked 46/4103 in IEEEXtreme 24-Hour Programming Competition.
Meritorious Winner in 2019 MCM/ICM Mathematical Contest in Modeling.

Last update on Sep 10th, 2023.