Abstract
A serious ecological hazard of illegal transactions (money laundering, financial fraud, etc.) on the Bitcoin trading network. Anti-money laundering and fraud detection are essential instruments to address the problem. However, such datasets are generally extremely unbalanced in terms of positive and negative samples, and most of the data are unlabelled, with the illegal class accounting for just a minimal fraction of the total, which prevents supervised learning from learning a well-represented feature. We propose a self-supervised learning framework based on contrastive learning, in which two different augmented transformations are applied to the original graph data, perturbations are randomly attached to the node features of the upgraded views, and the model parameters and perturbations are updated by gradient descent to maximize the consistency of the single node in different views. The experimental result demonstrates that our model achieves excellent performance in all metrics and is comparable to supervised methods, which verifies the efficiency of the perturbation-based contrastive learning model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cai, L., et al.: Structural temporal graph neural networks for anomaly detection in dynamic graphs. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3747–3756 (2021)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. ACM (2016)
Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84–92. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24261-3_7
Huang, Q., He, H., Singh, A., Lim, S.N., Benson, A.R.: Combining label propagation and simple models out-performs graph neural networks. arXiv preprint. arXiv:2010.13993 (2020)
Iscen, A., Tolias, G., Avrithis, Y., Chum, O.: Label propagation for deep semi-supervised learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5070–5079 (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint. arXiv:1609.02907 (2016)
Liu, Y., Li, Z., Pan, S., Gong, C., Zhou, C., Karypis, G.: Anomaly detection on attributed networks via contrastive self-supervised learning. IEEE Trans. Neural Netw. Learn. Syst. 33(6), 2378–2392 (2021)
Lorenz, J., Silva, M.I., Aparício, D., Ascensão, J.T., Bizarro, P.: Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. In: Proceedings of the First ACM International Conference on AI in Finance, pp. 1–8 (2020)
Melekhov, I., Kannala, J., Rahtu, E.: Siamese network features for image matching. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 378–383. IEEE (2016)
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Decentralized Bus. Rev. 21260 (2008)
Noorshams, N., Verma, S., Hofleitner, A.: Ties: temporal interaction embeddings for enhancing social media integrity at facebook. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3128–3135 (2020)
Pareja, A., et al.: Evolvegcn: evolving graph convolutional networks for dynamic graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5363–5370 (2020)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. ACM (2014)
Velickovic, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. ICLR (Poster) 2(3), 4 (2019)
Wang, F., Zhang, C.: Label propagation through linear neighborhoods. IEEE Trans. Knowl. Data Eng. 20(1), 55–67 (2007)
Wang, X., et al.: Apan: asynchronous propagation attention network for real-time temporal graph embedding. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2628–2638 (2021)
Wang, Y., Jin, J.: Bag of tricks of semi-supervised classification with graph neural networks. arXiv abs/2103.13355 (2021)
Wang, Y., Zhang, J., Guo, S., Yin, H., Li, C., Chen, H.: Decoupling representation learning and classification for gnn-based anomaly detection. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1239–1248 (2021)
Weber, M., et al.: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics. arXiv preprint. arXiv:1908.02591 (2019)
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. Adv. Neural. Inf. Process. Syst. 33, 5812–5823 (2020)
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Deep graph contrastive representation learning. In: ICML Workshop on Graph Representation Learning and Beyond (2020). http://arxiv.org/abs/2006.04131
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, Z., Chen, H., Peng, K. (2023). AP-GCL: Adversarial Perturbation on Graph Contrastive Learning. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13655. Springer, Cham. https://doi.org/10.1007/978-3-031-20096-0_47
Download citation
DOI: https://doi.org/10.1007/978-3-031-20096-0_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20095-3
Online ISBN: 978-3-031-20096-0
eBook Packages: Computer ScienceComputer Science (R0)