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abstract

Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection

Published: 14 August 2022 Publication History

Abstract

Deep graph learning (DGL) has achieved remarkable progress in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery. Despite the progress, how to ensure various DGL algorithms behave in a socially responsible manner and meet regulatory compliance requirements becomes an emerging problem, especially in risk-sensitive domains. Trustworthy graph learning (TwGL) aims to solve the above problems from a technical viewpoint. In contrast to conventional graph learning which mainly cares about model performance, TwGL considers various reliability and safety aspects of DGL, including but not limited to adversarial robustness, explainability, and privacy protection. Whilst several previous tutorials have been made for the introduction of DGL in KDD, seldom is there a special focus on its safety aspects, including reliability, explainability, and privacy protection capability. This tutorial mainly covers the key achievements of trustworthy graph learning in recent years. Specifically, we will discuss three essential topics, that is, the reliability of DGL against inherent noise, distribution shift and adversarial attack, explainability methods, and privacy protection for DGL. Meanwhile, we will introduce some guidelines for applying DGL to risk-sensitive applications (e.g., AI drug discovery) to ensure GNN models behave in a trustworthy way. We hope our tutorial can offer a comprehensive review of recent advances in this area and also provide some useful suggestions to guide the developers to choose appropriate techniques for their applications.

References

[1]
Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, and Zibin Zheng. 2020. A Survey of Adversarial Learning on Graphs. CoRR abs/2003.05730 (2020).
[2]
Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr. 2021. FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks. CoRR abs/2104.07145 (2021).
[3]
Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, and Peilin Zhao. 2022. A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. CoRR abs/2205.10014 (2022).
[4]
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2020. Explainability in Graph Neural Networks: A Taxonomic Survey. CoRR abs/2012.15445 (2020).

Cited By

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  • (2024)A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and ApplicationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.345432836:12(7497-7515)Online publication date: Dec-2024
  • (2024)Local structure-aware graph contrastive representation learningNeural Networks10.1016/j.neunet.2023.12.037172(106083)Online publication date: Apr-2024
  • (2024)Towards Distributed Graph Representation LearningComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-9637-7_41(547-557)Online publication date: 5-Jan-2024
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      cover image ACM Conferences
      KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
      August 2022
      5033 pages
      ISBN:9781450393850
      DOI:10.1145/3534678
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 14 August 2022

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      View all
      • (2024)A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and ApplicationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.345432836:12(7497-7515)Online publication date: Dec-2024
      • (2024)Local structure-aware graph contrastive representation learningNeural Networks10.1016/j.neunet.2023.12.037172(106083)Online publication date: Apr-2024
      • (2024)Towards Distributed Graph Representation LearningComputer Supported Cooperative Work and Social Computing10.1007/978-981-99-9637-7_41(547-557)Online publication date: 5-Jan-2024
      • (2024)A survey of out‐of‐distribution generalization for graph machine learning from a causal viewAI Magazine10.1002/aaai.12202Online publication date: 18-Oct-2024
      • (2023)SAM: Query-efficient Adversarial Attacks against Graph Neural NetworksACM Transactions on Privacy and Security10.1145/361130726:4(1-19)Online publication date: 13-Nov-2023
      • (2023)Semantic Interpretation and Validation of Graph Attention-Based Explanations for GNN Models2023 21st International Conference on Advanced Robotics (ICAR)10.1109/ICAR58858.2023.10406370(375-380)Online publication date: 5-Dec-2023
      • (2023)A Robust Detection and Correction Framework for GNN-Based Vertical Federated LearningPattern Recognition and Computer Vision10.1007/978-981-99-8435-0_8(97-108)Online publication date: 13-Oct-2023

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