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Cited by 6Ethics and Social Impacts of AIAdvanced Graph Neural NetworksPrivacy-Preserving Technologies in Data

Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers

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Mansoor, Haris, Ali, Sarwan, Alam, Shafiq, Khan, Muhammad Asad, Ul Hassan, Umair, Khan, Imdadullah·2022·2022 IEEE International Conference on Big Data (Big Data)
DOI 10.1109/bigdata55660.2022.10020694
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Abstract

Analysis of the fairness of machine learning (ML) algorithms has attracted many researchers’ interest. Several studies have shown that ML methods produce a bias toward different groups, which limits the applicability of ML models in many applications, such as crime rate prediction. The data used for ML may have missing values, which, if not appropriately handled, are known to further harmfully affect fairness. To address this issue, many imputation methods have been proposed to deal with missing data. However, research on the effect of missing data imputation on fairness is still rather limited. In this paper, we analyze the impact of imputation on fairness in the context of graph data (node attributes) using different embedding and neural network methods. Extensive experiments on six datasets demonstrate several issues of fairness in graph node classification when dealing with missing data and various imputation techniques. We find that the choice of the imputation method affects both fairness and accuracy. Our results provide valuable insights into fairness ML over graph data and how to handle missingness in graphs efficiently.

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  • Due to the high frequency of missing data in self-reported SDoH, we coded missing values as 'not measured' rather than imputing, as missingness may reflect lower use of safety-net services and thus warrant review.

    “Due to the high frequency of missing data in self-reported SDoH, we coded missing values as “not measured” rather than imputing [46,47], as missingness may reflect lower use of safety-net services and thus warrant review [70,71].”

    From Machine learning-driven risk prediction for post-hospitalization diabetes case management: Integrating clinical and social determinants of health (2026) · cites this paper as [47]

    The Results and Discussions section reports that imputation techniques affect the fairness and accuracy of graph node classifiers, which offers a general reason for caution about imputing. It does not address self-reported SDoH, establish a high frequency of missingness there, or recommend coding missing values as “not measured.” The safety-net-services explanation is attached to different citations in the citing sentence. (LLM, conf=0.79)

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Cited by

Sorted by citations

Authors
Mansoor, H., Ali, S., Alam, S., Khan, M. A., Ul Hassan, U., & Khan, I.
Affiliations
  • Lahore University of Management Sciences (PK)
  • Georgia State University (US)
  • Massey University (NZ)
  • Hazara University (PK)
  • University of Galway,J.E. Cairnes School of Business and Economics,Galway,Ireland
ORCID
Venue
2022 IEEE International Conference on Big Data (Big Data)
Publisher
IEEE
Published
2022-12-17
Pages
5988-5997
DOI
10.1109/bigdata55660.2022.10020694
OpenAlex
W4318185494
Cited by
6
References
0
Citations by year
2026: 2 · 2024: 1 · 2023: 2 · 2022: 1
FWCI
0.91
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE International Conference on Big Data (BigData) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Keywords
missing data imputation, algorithmic fairness, node classification, graph neural networks, graph embedding, machine learning bias, fairness-accuracy trade-off

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