FG2 AN: Fairness-Aware Graph Generative Adversarial Networks

Zichong WANG, Charles WALLACE, Albert BIFET, Xin YAO, Wenbin ZHANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

3 Citations (Scopus)


Graph generation models have gained increasing popularity and success across various domains. However, most research in this area has concentrated on enhancing performance, with the issue of fairness remaining largely unexplored. Existing graph generation models prioritize minimizing graph reconstruction’s expected loss, which can result in representational disparities in the generated graphs that unfairly impact marginalized groups. This paper addresses this socially sensitive issue by conducting the first comprehensive investigation of fair graph generation models by identifying the root causes of representational disparities, and proposing a novel framework that ensures consistent and equitable representation across all groups. Additionally, a suite of fairness metrics has been developed to evaluate bias in graph generation models, standardizing fair graph generation research. Through extensive experiments on five real-world datasets, the proposed framework is demonstrated to outperform existing benchmarks in terms of graph fairness while maintaining competitive prediction performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases : Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part II
EditorsDanai KOUTRA, Claudia PLANT, Manuel Gomez RODRIGUEZ, Elena BARALIS, Francesco BONCHI
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Electronic)9783031434150
ISBN (Print)9783031434143
Publication statusPublished - 2023
Externally publishedYes
EventThe 2023 edition of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Turin, Italy
Duration: 18 Sept 202322 Sept 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
ISSN (Print)2945-9133
ISSN (Electronic)2945-9141


ConferenceThe 2023 edition of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Abbreviated titleECML PKDD 2023


  • Algorithmic Fairness
  • Graph Generation
  • Graph Mining


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