Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers-Reference-Cited by-同舟云学术

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

Published:2022-12-17 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Big Data (Big Data)
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Author:

Mansoor Haris¹,Ali Sarwan²,Alam Shafiq³,Khan Muhammad Asad⁴,Ul Hassan Umair⁵,Khan Imdadullah¹

Affiliation:

1. Lahore University of Management Science,Lahore,Pakistan

2. Georgia State University,Atlanta,USA

3. Massey University,Massey Business School,Auckland,New Zealand

4. Hazara University,Mansehra,Pakistan

5. University of Galway,J.E. Cairnes School of Business and Economics,Galway,Ireland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10020192/10020156/10020694.pdf?arnumber=10020694

Reference47 articles.

1. EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks

2. Missing data imputation with adversarially-trained graph convolutional networks

3. Convolutional neural networks on graphs with fast localized spectral filtering;defferrard;Advances in neural information processing systems,2016

4. Ensuring Fairness in Machine Learning to Advance Health Equity

5. Censoring representations with an adversary;edwards,2015

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1. Trustworthy Graph Neural Networks: Aspects, Methods, and Trends;Proceedings of the IEEE;2024-02

2. IFGNN: An Individual Fairness Awareness Model for Missing Sensitive Information Graphs;Lecture Notes in Computer Science;2023-11-07

3. Towards Developing an Automated Chatbot for Predicting Legal Case Outcomes: A Deep Learning Approach;Intelligent Information and Database Systems;2023

4. Evaluating COVID-19 Sequence Data Using Nearest-Neighbors Based Network Model;2022 IEEE International Conference on Big Data (Big Data);2022-12-17

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