Abstract
Graph anomaly detection is an important aspect of anomaly detection, especially the graph-level anomaly detection, which can be used into biomolecular research or drug molecular detection and so on. It is necessary to identify anomalies within a limited sample size as the real-world scenarios always lack of anomalous graph labels. Graph-level anomaly detection with few samples mainly faces the following problems: 1) There are not enough samples for the model to effectively learn anomalies; 2) There is noise or irrelevant information in the graph, which makes it difficult to quickly learn the key structural information of the graph. To address these issues, we propose a Robust Meta-learning-based Graph Anomaly Detection Framework via Graph Coarsening (RCM-GAD). Specifically, we employ meta-learning to effectively extract and integrate abnormal information from similar networks. Then, we use the Graph Coarsening module to obtain the key structural information of the graph for anomaly detection. We apply this framework to detect anomalies at both the graph-level and subgraph-level. We conduct experiments on four datasets, demonstrating the superiority of our proposed framework, RCM-GAD, over state-of-the-art baselines in graph-level and subgraph-level anomaly detection tasks.
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References
Chen, M., Zhang, W., Zhang, W., Chen, Q., Chen, H.: Meta relational learning for few-shot link prediction in knowledge graphs. arXiv preprint arXiv:1909.01515 (2019)
Cheng, H., Zhou, J.T., Tay, W.P., Wen, B.: Graph neural networks with triple attention for few-shot learning. IEEE Transactions on Multimedia (2023)
Debnath, A.K., de Compadre, R.L.L., Debnath, G., Shusterman, A.J., Hansch, C.: Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity. J. Med. Chemis. 34(2), 786–797 (1991)
Ding, K., Zhou, Q., Tong, H., Liu, H.: Few-shot network anomaly detection via cross-network meta-learning. In: Proceedings of the Web Conference 2021, pp. 2448–2456 (2021)
Du, H., Li, D., Wang, W.: Abnormal user detection via multiview graph clustering in the mobile e-commerce network. Wireless Communications and Mobile Computing 2022 (2022)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, pp. 1126–1135. PMLR (2017)
Franceschi, L., Niepert, M., Pontil, M., He, X.: Learning discrete structures for graph neural networks. In: International Conference on Machine Learning, pp. 1972–1982. PMLR (2019)
Guo, Q., Zhao, X., Fang, Y., Yang, S., Lin, X., Ouyang, D.: Learning hypersphere for few-shot anomaly detection on attributed networks. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 635–645 (2022)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems 30 (2017)
Helma, C., King, R.D., Kramer, S., Srinivasan, A.: The Predictive Toxicology Challenge 2000-2001. Bioinformatics 17(1), 107–108 (2001). https://doi.org/10.1093/bioinformatics/17.1.107. http://bioinformatics.oxfordjournals.org/cgi/doi/10.1093/bioinformatics/17.1.107
Hooi, B., Song, H.A., Beutel, A., Shah, N., Shin, K., Faloutsos, C.: FRAUDAR: bounding graph fraud in the face of camouflage. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 895–904 (2016)
Lee, C.Y., Chen, Y.P.P.: Descriptive prediction of drug side-effects using a hybrid deep learning model. Int. J. Intell. Syst. 36(6), 2491–2510 (2021)
Loukas, A.: Graph reduction with spectral and cut guarantees. J. Mach. Learn. Res. 20(116), 1–42 (2019)
Luo, X., et al.: Deep graph level anomaly detection with contrastive learning. Sci. Rep. 12(1), 19867 (2022)
Ma, R., Pang, G., Chen, L., van den Hengel, A.: Deep graph-level anomaly detection by glocal knowledge distillation. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 704–714 (2022)
Ma, X., et al.: A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering (2021)
Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999 (2018)
Pang, G., Shen, C., van den Hengel, A.: Deep anomaly detection with deviation networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 353–362 (2019)
Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: AAAI (2015). https://networkrepository.com/
Wang, H., Zhou, C., Wu, J., Dang, W., Zhu, X., Wang, J.: Deep structure learning for fraud detection. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 567–576. IEEE (2018)
Wang, S., Chen, C., Li, J.: Graph few-shot learning with task-specific structures. arXiv preprint arXiv:2210.12130 (2022)
Wang, S., Dong, Y., Ding, K., Chen, C., Li, J.: Few-shot node classification with extremely weak supervision. arXiv preprint arXiv:2301.02708 (2023)
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861–6871. PMLR (2019)
Xiong, W., Yu, M., Chang, S., Guo, X., Wang, W.Y.: One-shot relational learning for knowledge graphs. arXiv preprint arXiv:1808.09040 (2018)
Yao, H., et al.: Graph few-shot learning via knowledge transfer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 6656–6663 (2020)
Zafarani, R., Abbasi, M.A., Liu, H.: Social media mining: an introduction. Cambridge University Press (2014)
Zhang, G., et al.: Dual-discriminative graph neural network for imbalanced graph-level anomaly detection. Adv. Neural. Inf. Process. Syst. 35, 24144–24157 (2022)
Zhang, Z., Zhao, L.: Unsupervised deep subgraph anomaly detection. In: 2022 IEEE International Conference on Data Mining (ICDM), pp. 753–762. IEEE (2022)
Zhou, F., Cao, C., Zhang, K., Trajcevski, G., Zhong, T., Geng, J.: Meta-GNN: on few-shot node classification in graph meta-learning. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2357–2360 (2019)
Zügner, D., Günnemann, S.: Adversarial attacks on graph neural networks via meta learning (2019)
Acknowledgement
This work is supported by the Project of Shenzhen Higher Education Stability Support Program (No.20220618160306001) and NSFC program (No. 62272338).
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Li, L., Sun, Y., Li, T., Shao, M. (2023). Robust Few-Shot Graph Anomaly Detection via Graph Coarsening. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14117. Springer, Cham. https://doi.org/10.1007/978-3-031-40283-8_35
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