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Fair Graph Representation Learning with Imbalanced and Biased Data

Published: 15 February 2022 Publication History

Abstract

Graph-structured data is omnipresent in various fields, such as biology, chemistry, social media and transportation. Learning informative graph representations are crucial in effectively completing downstream graph-related tasks such as node/graph classification and link prediction. Graph Neural Networks (GNNs), due to their inclusiveness on handling graph-structured data and distinguished data-mining power inherited from deep learning, have achieved significant success in learning graph representations. Nonetheless, most existing GNNs are mainly designed with unrealistic data assumptions, such as the balanced and unbiased data distributions while abounding real-world networks exhibit skewed (i.e., long-tailed) node/graph class distributions and may also encode patterns of previous discriminatory decisions dominated by sensitive attributes. Even further, extensive research efforts have been invested in developing GNN architectures towards improving model utility while most of the time totally ignoring whether the obtained node/graph representations conceal any discriminatory bias, which could lead to prejudicial decisions as GNN-based machine learning models are increasingly being utilized in real-world applications. In light of the prevalence of the above two types of unfairness originated from quantity-imbalanced and discriminatory bias, my research expects to propose novel node/graph representation learning frameworks through constructing innovative GNN architectures and devising novel graph-mining algorithms to learn both fair and expressive node/graph representations that can enjoy a favorable fairness-utility tradeoff.

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Graph Neural Networks (GNNs) have achieved unprecedented success in learning node/graph representations to complete classification tasks. Nonetheless, most existing GNNs are designed assuming the data is balanced while abounding real-world networks exhibit skewed class distributions. Even further, extensive research work on developing GNNs towards improving model utility while totally ignoring whether the obtained node representations conceal any discriminatory bias, which could lead to prejudicial decisions in real-world applications. In light of the above two types of unfairness originated from quantity-imbalanced and discriminatory bias, my research expects to propose novel node/graph representation learning frameworks through constructing innovative GNNs and devising novel graph-mining algorithms to learn both fair and expressive node/graph representations that can enjoy a favorable fairness-utility tradeoff.

References

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Enyan Dai and Suhang Wang. 2021. Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining .
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Yushun Dong, Ninghao Liu, Brian Jalaian, and Jundong Li. 2021. EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks. arXiv preprint arXiv:2108.05233 .
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Yu Wang, Wei Jin, and Tyler Derr. 2021 b. Graph Neural Networks: Self-supervised Learning. Graph Neural Networks: Foundations, Frontiers, and Applications, Lingfei Wu, Peng Cui, Jian Pei, and Liang Zhao (Eds.). Springer, Singapore (2021), 391--419.
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    cover image ACM Conferences
    WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
    February 2022
    1690 pages
    ISBN:9781450391320
    DOI:10.1145/3488560
    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: 15 February 2022

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    Author Tags

    1. fairness node/graph representation learning
    2. graph neural networks
    3. imbalanced node/graph classification

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    • (2025)An analysis of graph neural networks for fake review detection: A systematic literature reviewNeurocomputing10.1016/j.neucom.2025.129341(129341)Online publication date: Jan-2025
    • (2024)Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer’s Disease StagingIEEE Transactions on Medical Imaging10.1109/TMI.2024.338974743:9(3126-3136)Online publication date: Sep-2024
    • (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)Addressing imbalance in graph datasets: Introducing GATE-GNN with graph ensemble weight attention and transfer learning for enhanced node classificationExpert Systems with Applications10.1016/j.eswa.2024.124602255(124602)Online publication date: Dec-2024
    • (2024)A novel graph oversampling framework for node classification in class-imbalanced graphsScience China Information Sciences10.1007/s11432-023-3897-267:6Online publication date: 15-Apr-2024
    • (2023)Collaboration-Aware Graph Convolutional Network for Recommender SystemsProceedings of the ACM Web Conference 202310.1145/3543507.3583229(91-101)Online publication date: 30-Apr-2023
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    • (2022)Graph Learning for Fake Review DetectionFrontiers in Artificial Intelligence10.3389/frai.2022.9225895Online publication date: 20-Jun-2022
    • (2022)Degree-Related Bias in Link Prediction2022 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW58026.2022.00103(757-758)Online publication date: Nov-2022

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