Abstract
Graph neural networks (GNNs) have demonstrated remarkable success in addressing a variety of node classification problems. Cross-network node classification (CNNC) extends the GNN formulation to a multi-network setting, enabling the classification to be performed on an unlabeled target network. However, applying GNNs to a multi-network setting in practice is a challenge due to the possible presence of concept drift and the need to account for link biases in the graph data. In this paper we present FOCI, a powerful, model-agnostic approach for cross-network node classification that enables the GNN to overcome the concept drift issue while mitigating potential biases in the data. FOCI utilizes a fair Sinkhorn distance function with optimal transport to learn a fair yet effective feature embedding of the nodes in the source graph. We experimentally demonstrate the effectiveness of FOCI at addressing the CNNC task while simultaneously mitigating unfairness compared to other baseline methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Angwin, J., Larson, J., Mattu, S., Kirchner, L.: Machine bias. ProPublica (2016)
Cao, S., Lu, W., Xu, Q.: Deep neural networks for learning graph representations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)
Courty, N., Flamary, R., Tuia, D.: Domain adaptation with regularized optimal transport. In: Proceedings of ECML PKDD, pp. 274–289 (2014)
Craddock, C., et al.: Towards automated analysis of connectomes: the configurable pipeline for the analysis of connectomes (C-PAC). Front. Neuroinform. 42, 10–3389 (2013)
Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Advances in Neural Information Processing Systems, vol. 26 (2013)
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)
Dong, Y., Lizardo, O., Chawla, N.V.: Do the young live in a “smaller world” than the old? age-specific degrees of separation in a large-scale mobile communication network. arXiv preprint arXiv:1606.07556 (2016)
Kantorovitch, L.: On the translocation of masses. Manage. Sci. 5(1), 1–4 (1958). https://doi.org/10.1287/mnsc.5.1.1
Karimi, F., Génois, M., Wagner, C., Singer, P., Strohmaier, M.: Homophily influences ranking of minorities in social networks. Sci. Rep. 8(1), 11077 (2018)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data (2014)
Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning (2018)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)
Parisot, S., et al.: Spectral graph convolutions for population-based disease prediction. In: Proceedings of MICCAI, pp. 177–185 (2017)
Rahman, T., Surma, B., Backes, M., Zhang, Y.: Fairwalk: Towards fair graph embedding. In: Proceedings of IJCAI, pp. 3289–3295 (2019)
Shen, X., Dai, Q., Chung, F.l., Lu, W., Choi, K.S.: Adversarial deep network embedding for cross-network node classification. In: Proceedings of AAAI Conference on Artificial Intelligence, vol. 34, pp. 2991–2999 (2020)
Shen, X., Dai, Q., Mao, S., Chung, F.l., Choi, K.S.: Network together: Node classification via cross-network deep network embedding. IEEE Trans. Neural Netw. Learn. Syst. 32(5), 1935–1948 (2020)
Yeh, I.C., Lien, C.H.: The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst. Appl. 36(2), 2473–2480 (2009)
Zhang, X., Du, Y., Xie, R., Wang, C.: Adversarial separation network for cross-network node classification. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2618–2626 (2021)
Acknowledgment
This material is based upon work supported by NSF under grant #IIS-1939368 and #IIS-2006633. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Stephens, A., Santos, F., Tan, PN., Esfahanian, AH. (2025). FOCI: Fair Cross-Network Node Classification via Optimal Transport. In: Aiello, L.M., Chakraborty, T., Gaito, S. (eds) Social Networks Analysis and Mining. ASONAM 2024. Lecture Notes in Computer Science, vol 15212. Springer, Cham. https://doi.org/10.1007/978-3-031-78538-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-78538-2_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-78537-5
Online ISBN: 978-3-031-78538-2
eBook Packages: Computer ScienceComputer Science (R0)