Abstract
A number of approaches for anomaly detection on attributed networks have been proposed. However, most of them suffer from two major limitations: (1) they rely on unsupervised approaches which are intrinsically less effective due to the lack of supervisory signals of what information is relevant for capturing anomalies, and (2) they rely only on using local, e.g., one- or two-hop away node neighbourhood information, but ignore the more global context. Since anomalous nodes differ from normal nodes in structures and attributes, it is intuitive that the distance between anomalous nodes and their neighbors should be larger than that between normal nodes and their (also normal) neighbors if we remove the edges connecting anomalous and normal nodes. Thus, estimating hop counts based on both global and local contextual information can help us to construct an anomaly indicator. Following this intuition, we propose a hop-count based model (HCM) that achieves that. Our approach includes two important learning components: (1) Self-supervised learning task of predicting the shortest path length between a pair of nodes, and (2) Bayesian learning to train HCM for capturing uncertainty in learned parameters and avoiding overfitting. Extensive experiments on real-world attributed networks demonstrate that HCM consistently outperforms state-of-the-art approaches.
T. Huang and Y. Pei—Both authors contributed equally to this research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104 (2000)
Brochier, R., Guille, A., Velcin, J.: Link prediction with mutual attention for text-attributed networks. In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 283–284 (2019)
Ding, K., Li, J., Bhanushali, R., Liu, H.: Deep anomaly detection on attributed networks. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 594–602. SIAM (2019)
Ding, K., Li, J., Liu, H.: Interactive anomaly detection on attributed networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 357–365 (2019)
Falih, I., Grozavu, N., Kanawati, R., Bennani, Y.: Community detection in attributed network. In: Companion Proceedings of the The Web Conference 2018, pp. 1299–1306 (2018)
Gao, J., Liang, F., Fan, W., Wang, C., Sun, Y., Han, J.: On community outliers and their efficient detection in information networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 813–822 (2010)
Gutiérrez-Gómez, L., Bovet, A., Delvenne, J.C.: Multi-scale anomaly detection on attributed networks. arXiv preprint arXiv:1912.04144 (2019)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024–1034 (2017)
Hendrycks, D., Mazeika, M., Kadavath, S., Song, D.: Using self-supervised learning can improve model robustness and uncertainty. In: Advances in Neural Information Processing Systems, pp. 15663–15674 (2019)
Huang, X., Li, J., Hu, X.: Accelerated attributed network embedding. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 633–641. SIAM (2017)
Jin, W., Derr, T., Liu, H., Wang, Y., Wang, S., Liu, Z., Tang, J.: Self-supervised learning on graphs: Deep insights and new direction. arXiv preprint arXiv:2006.10141 (2020)
Johnson, D.B.: Efficient algorithms for shortest paths in sparse networks. J. ACM (JACM) 24(1), 1–13 (1977)
Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574–5584 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Li, J., Cheng, K., Wu, L., Liu, H.: Streaming link prediction on dynamic attributed networks. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 369–377 (2018)
Li, J., Dani, H., Hu, X., Liu, H.: Radar: residual analysis for anomaly detection in attributed networks. In: IJCAI, pp. 2152–2158 (2017)
Liang, J., Jacobs, P., Sun, J., Parthasarathy, S.: Semi-supervised embedding in attributed networks with outliers. In: Proceedings of the 2018 SIAM International Conference on Data Mining, pp. 153–161. SIAM (2018)
Meng, Z., Liang, S., Bao, H., Zhang, X.: Co-embedding attributed networks. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 393–401 (2019)
Metsis, V., Androutsopoulos, I., Paliouras, G.: Spam filtering with Naive Bayes-which naive bayes? In: CEAS, vol. 17, pp. 28–69. Mountain View, CA (2006)
Müller, E., Sánchez, P.I., Mülle, Y., Böhm, K.: Ranking outlier nodes in subspaces of attributed graphs. In: 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), pp. 216–222. IEEE (2013)
Pei, Y., Chakraborty, N., Sycara, K.: Nonnegative matrix tri-factorization with graph regularization for community detection in social networks. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)
Pei, Y., Huang, T., van Ipenburg, W., Pechenizkiy, M.: RESGCN: attention-based deep residual modeling for anomaly detection on attributed networks. arXiv preprint arXiv:2009.14738 (2020)
Peng, Z., Dong, Y., Luo, M., Wu, X.M., Zheng, Q.: Self-supervised graph representation learning via global context prediction. arXiv preprint arXiv:2003.01604 (2020)
Peng, Z., Luo, M., Li, J., Liu, H., Zheng, Q.: Anomalous: a joint modeling approach for anomaly detection on attributed networks. In: IJCAI, pp. 3513–3519 (2018)
Perozzi, B., Akoglu, L.: Scalable anomaly ranking of attributed neighborhoods. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 207–215. SIAM (2016)
Perozzi, B., Akoglu, L., Iglesias Sánchez, P., Müller, E.: Focused clustering and outlier detection in large attributed graphs. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1346–1355 (2014)
Song, X., Wu, M., Jermaine, C., Ranka, S.: Conditional anomaly detection. IEEE Trans. Knowl. Data Eng. 19(5), 631–645 (2007)
Welling, M., Teh, Y.W.: Bayesian learning via stochastic gradient Langevin dynamics. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 681–688 (2011)
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987)
Wu, H., Wang, C., Tyshetskiy, Y., Docherty, A., Lu, K., Zhu, L.: Adversarial examples for graph data: deep insights into attack and defense. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 4816–4823. AAAI Press (2019)
You, Y., Chen, T., Wang, Z., Shen, Y.: When does self-supervision help graph convolutional networks? arXiv preprint arXiv:2006.09136 (2020)
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
Huang, T., Pei, Y., Menkovski, V., Pechenizkiy, M. (2023). Hop-Count Based Self-supervised Anomaly Detection on Attributed Networks. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham. https://doi.org/10.1007/978-3-031-26387-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-26387-3_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26386-6
Online ISBN: 978-3-031-26387-3
eBook Packages: Computer ScienceComputer Science (R0)