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Original Article
Graph Neural Networks for Cyber Threat Prediction in Financial Networks
Dr. Rajan Nagarajan1
1 Artificial Intelligence and Machine Learning, Madras University, Tamil Nadu, India.
Published Online: May-June 2026
Pages: 175-180
Cite this article
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1. Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009). The graph neural network model. IEEE Transactions on Neural Networks, 20(1), 61–80.
2. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017). OpenReview.
3. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2018). Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR 2018). OpenReview.
4. Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems (NeurIPS 2017), 1024–1034.
5. Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How powerful are graph neural networks? In Proceedings of the 7th International Conference on Learning Representations (ICLR 2019). OpenReview.
6. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57–81.
7. Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., & Leiserson, C. E. (2020). EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5363–5370.
8. Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564–608.
9. Pourhabibi, T., Ong, K. L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, 113307.
10. Weber, M., Domeniconi, G., Chen, J., Weidele, D. K. I., Bellei, C., Robinson, T., & Leiserson, C. E. (2019). Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591.
11. Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., & He, Q. (2021). Pick-and-choose: A GNN-based imbalanced learning approach for fraud detection. In Proceedings of the Web Conference 2021 (WWW '21), 3168–3177. ACM.
12. Dou, Y., Liu, Z., Sun, L., Deng, J., Peng, H., & Yu, P. S. (2020). Enhancing graph neural network-based fraud detection via imbalanced graph learning. In Proceedings of the Web Conference 2020 (WWW '20). ACM.
13. Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.
14. IBM Security. (2023). Cost of a Data Breach Report 2023. IBM Corporation. Retrieved from https://www.ibm.com/reports/data-breach
15. Financial Stability Board. (2022). Cyber Incident Reporting: Existing Approaches and Options for Greater Convergence. FSB Thematic Review Report.
16. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393(6684), 440–442.
17. Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017), 2980–2988.
18. Ying, R., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating explanations for graph neural networks. In Advances in Neural Information Processing Systems (NeurIPS 2019), 9244–9255.
19. Zugner, D., Akbarnejad, A., & Gunnemann, S. (2018). Adversarial attacks on neural networks for graph data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), 2847–2856.
2. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017). OpenReview.
3. Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2018). Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR 2018). OpenReview.
4. Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems (NeurIPS 2017), 1024–1034.
5. Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How powerful are graph neural networks? In Proceedings of the 7th International Conference on Learning Representations (ICLR 2019). OpenReview.
6. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57–81.
7. Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., & Leiserson, C. E. (2020). EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 5363–5370.
8. Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564–608.
9. Pourhabibi, T., Ong, K. L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, 113307.
10. Weber, M., Domeniconi, G., Chen, J., Weidele, D. K. I., Bellei, C., Robinson, T., & Leiserson, C. E. (2019). Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591.
11. Liu, Y., Ao, X., Qin, Z., Chi, J., Feng, J., Yang, H., & He, Q. (2021). Pick-and-choose: A GNN-based imbalanced learning approach for fraud detection. In Proceedings of the Web Conference 2021 (WWW '21), 3168–3177. ACM.
12. Dou, Y., Liu, Z., Sun, L., Deng, J., Peng, H., & Yu, P. S. (2020). Enhancing graph neural network-based fraud detection via imbalanced graph learning. In Proceedings of the Web Conference 2020 (WWW '20). ACM.
13. Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.
14. IBM Security. (2023). Cost of a Data Breach Report 2023. IBM Corporation. Retrieved from https://www.ibm.com/reports/data-breach
15. Financial Stability Board. (2022). Cyber Incident Reporting: Existing Approaches and Options for Greater Convergence. FSB Thematic Review Report.
16. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393(6684), 440–442.
17. Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017), 2980–2988.
18. Ying, R., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating explanations for graph neural networks. In Advances in Neural Information Processing Systems (NeurIPS 2019), 9244–9255.
19. Zugner, D., Akbarnejad, A., & Gunnemann, S. (2018). Adversarial attacks on neural networks for graph data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), 2847–2856.
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