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\(\mathrm FG^2AN\): Fairness-Aware Graph Generative Adversarial Networks

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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14170))

Abstract

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.

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References

  1. Akoglu, L., McGlohon, M., Faloutsos, C.: RTM: laws and a recursive generator for weighted time-evolving graphs. In: 2008 Eighth IEEE International Conference on Data Mining. pp. 701–706. IEEE (2008)

    Google Scholar 

  2. Aksoy, S.G., Purvine, E., Cotilla-Sanchez, E., Halappanavar, M.: A generative graph model for electrical infrastructure networks. J. Complex Netw. 7(1), 128–162 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  3. Alam, M., Perumalla, K.S., Sanders, P.: Novel parallel algorithms for fast multi-GPU-based generation of massive scale-free networks. Data Sci. Eng. 4, 61–75 (2019)

    Article  Google Scholar 

  4. Barocas, S., Selbst, A.D.: Big data’s disparate impact. California law review pp. 671–732 (2016)

    Google Scholar 

  5. Beutel, A., Chen, J., Zhao, Z., Chi, E.H.: Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075 (2017)

  6. Binns, R.: Fairness in machine learning: Lessons from political philosophy. In: Conference on Fairness, Accountability and Transparency. pp. 149–159. PMLR (2018)

    Google Scholar 

  7. Bojchevski, A., Shchur, O., Zügner, D., Günnemann, S.: Netgan: Generating graphs via random walks. In: International conference on machine learning. pp. 610–619. PMLR (2018)

    Google Scholar 

  8. Bose, A., Hamilton, W.: Compositional fairness constraints for graph embeddings. In: International Conference on Machine Learning. pp. 715–724. PMLR (2019)

    Google Scholar 

  9. Buyl, M., De Bie, T.: Debayes: a bayesian method for debiasing network embeddings. In: International Conference on Machine Learning. pp. 1220–1229. PMLR (2020)

    Google Scholar 

  10. Cascio, W.F., Aguinis, H.: The federal uniform guidelines on employee selection procedures (1978) an update on selected issues. Rev. Public Pers. Adm. 21(3), 200–218 (2001)

    Article  Google Scholar 

  11. Chakrabarti, D., Faloutsos, C.: Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. (CSUR) 38(1), 2-es (2006)

    Google Scholar 

  12. Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53–65 (2018)

    Article  Google Scholar 

  13. Dai, E., Wang, S.: 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. pp. 680–688 (2021)

    Google Scholar 

  14. Farnad, G., Babaki, B., Gendreau, M.: A unifying framework for fairness-aware influence maximization. In: Companion Proceedings of the Web Conference 2020. pp. 714–722 (2020)

    Google Scholar 

  15. Fisher, J., Mittal, A., Palfrey, D., Christodoulopoulos, C.: Debiasing knowledge graph embeddings. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 7332–7345 (2020)

    Google Scholar 

  16. Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)

    Article  MathSciNet  Google Scholar 

  17. Guo, X., Zhao, L.: A systematic survey on deep generative models for graph generation. IEEE Trans. Pattern Anal. Mach. Intell. 45, 5370–5390 (2022)

    Google Scholar 

  18. He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, pp. 507–517 (2016)

    Google Scholar 

  19. Hofstra, B., Corten, R., Van Tubergen, F., Ellison, N.B.: Sources of segregation in social networks: a novel approach using Facebook. Am. Sociol. Rev. 82(3), 625–656 (2017)

    Article  Google Scholar 

  20. Kang, J., He, J., Maciejewski, R., Tong, H.: Inform: individual fairness on graph mining. In: Proceedings of the 26th ACM Sigkdd International Conference on Knowledge Discovery & Data Mining, pp. 379–389 (2020)

    Google Scholar 

  21. Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)

  22. Kleindessner, M., Samadi, S., Awasthi, P., Morgenstern, J.: Guarantees for spectral clustering with fairness constraints. In: International Conference on Machine Learning. pp. 3458–3467. PMLR (2019)

    Google Scholar 

  23. Liu, W., Chen, P.Y., Cooper, H., Oh, M.H., Yeung, S., Suzumura, T.: Can gan learn topological features of a graph? arXiv preprint arXiv:1707.06197 (2017)

  24. Louail, T., et al.: Uncovering the spatial structure of mobility networks. Nature Commun. 6(1), 6007 (2015)

    Article  Google Scholar 

  25. Ma, J., Guo, R., Mishra, S., Zhang, A., Li, J.: Clear: Generative counterfactual explanations on graphs. arXiv preprint arXiv:2210.08443 (2022)

  26. Ma, J., Guo, R., Wan, M., Yang, L., Zhang, A., Li, J.: Learning fair node representations with graph counterfactual fairness. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. pp. 695–703 (2022)

    Google Scholar 

  27. Rahman, T., Surma, B., Backes, M., Zhang, Y.: Fairwalk: towards fair graph embedding (2019)

    Google Scholar 

  28. Rawls, A.: Theories of social justice (1971)

    Google Scholar 

  29. Red, V., Kelsic, E.D., Mucha, P.J., Porter, M.A.: Comparing community structure to characteristics in online collegiate social networks. SIAM Rev. 53(3), 526–543 (2011)

    Article  MathSciNet  Google Scholar 

  30. Robins, G., Pattison, P.: Random graph models for temporal processes in social networks. J. Math. Sociol. 25(1), 5–41 (2001)

    Article  MATH  Google Scholar 

  31. Saxena, N.A., Zhang, W., Shahabi, C.: Missed opportunities in fair AI. In: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM). pp. 961–964. SIAM (2023)

    Google Scholar 

  32. Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93–93 (2008)

    Google Scholar 

  33. Simonovsky, M., Komodakis, N.: GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 412–422. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01418-6_41

    Chapter  Google Scholar 

  34. Tavakoli, S., Hajibagheri, A., Sukthankar, G.: Learning social graph topologies using generative adversarial neural networks. In: International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction (2017)

    Google Scholar 

  35. Tsioutsiouliklis, S., Pitoura, E., Tsaparas, P., Kleftakis, I., Mamoulis, N.: Fairness-aware pagerank. In: Proceedings of the Web Conference 2021. pp. 3815–3826 (2021)

    Google Scholar 

  36. Wan, H., Zhang, Y., Zhang, J., Tang, J.: AMiner: search and mining of academic social networks. Data Intell. 1(1), 58–76 (2019)

    Article  Google Scholar 

  37. Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. pp. 165–174 (2019)

    Google Scholar 

  38. Wang, Z., et al.: Preventing discriminatory decision-making in evolving data streams. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 149–159 (2023)

    Google Scholar 

  39. Wang, Z., Zhang, W.: Advancing fairness in machine learning: Multi-dimensional perspective and integrated evaluation framework (2023)

    Google Scholar 

  40. Wang, Z., Zhang, W.: Mitigating multisource biases in graph neural networks via real counterfactual instances (2023)

    Google Scholar 

  41. Wang, Z., et al.: Towards fair machine learning software: understanding and addressing model bias through counterfactual thinking. arXiv preprint arXiv:2302.08018 (2023)

  42. Wei, Y., Yildirim, P., Van den Bulte, C., Dellarocas, C.: Credit scoring with social network data. Mark. Sci. 35(2), 234–258 (2016)

    Article  Google Scholar 

  43. Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., Xie, X.: Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 726–735 (2021)

    Google Scholar 

  44. Ye, M., Liu, X., Lee, W.C.: Exploring social influence for recommendation: a generative model approach. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 671–680 (2012)

    Google Scholar 

  45. You, J., Liu, B., Ying, Z., Pande, V., Leskovec, J.: Graph convolutional policy network for goal-directed molecular graph generation. Adv. Neural Inf. Process. Syst. 31 (2018)

    Google Scholar 

  46. You, J., Ying, R., Ren, X., Hamilton, W., Leskovec, J.: Graphrnn: generating realistic graphs with deep auto-regressive models. In: International Conference on Machine Learning, pp. 5708–5717. PMLR (2018)

    Google Scholar 

  47. Zhang, S., et al.: Hidden: hierarchical dense subgraph detection with application to financial fraud detection. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 570–578. SIAM (2017)

    Google Scholar 

  48. Zhang, W., Hernandez-Boussard, T., Weiss, J.C.: Censored fairness through awareness. In: Proceedings of the AAAI Conference on Artificial Intelligence (2023)

    Google Scholar 

  49. Zhang, W., Kim, J., Wang, Z., Ravikumar, P., Weiss, J.: Individual fairness guarantee in learning with censorship (2023)

    Google Scholar 

  50. Zhang, W., Ntoutsi, E.: Faht: an adaptive fairness-aware decision tree classifier. arXiv preprint arXiv:1907.07237 (2019)

  51. Zhang, W., Pan, S., Zhou, S., Walsh, T., Weiss, J.C.: Fairness amidst non-iid graph data: Current achievements and future directions. arXiv preprint arXiv:2202.07170 (2022)

  52. Zhang, W., Weiss, J.C.: Longitudinal fairness with censorship. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 12235–12243 (2022)

    Google Scholar 

  53. Zhou, D., Zheng, L., Han, J., He, J.: A data-driven graph generative model for temporal interaction networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 401–411 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Florida International University, Miami, USA

    Zichong Wang & Wenbin Zhang

  2. Michigan Technological University, Houghton, USA

    Charles Wallace

  3. University of Waikato, Hamilton, New Zealand

    Albert Bifet

  4. Télécom Paris Tech, Palaiseau, France

    Albert Bifet

  5. Southern University of Science and Technology, Shenzhen, China

    Xin Yao

Authors
  1. Zichong Wang
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  2. Charles Wallace
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  3. Albert Bifet
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  4. Xin Yao
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  5. Wenbin Zhang
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Corresponding author

Correspondence to Wenbin Zhang .

Editor information

Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

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Wang, Z., Wallace, C., Bifet, A., Yao, X., Zhang, W. (2023). \(\mathrm FG^2AN\): Fairness-Aware Graph Generative Adversarial Networks. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14170. Springer, Cham. https://doi.org/10.1007/978-3-031-43415-0_16

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  • DOI: https://doi.org/10.1007/978-3-031-43415-0_16

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  • Print ISBN: 978-3-031-43414-3

  • Online ISBN: 978-3-031-43415-0

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