Skip to main content
Springer Nature Link
Log in

Anomaly Detection on Static and Dynamic Graphs Using Graph Convolutional Neural Networks

  • Chapter
  • First Online:
Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1030))

Abstract

Anomalies represent rare observations that vary significantly from others. Anomaly detection intended to discover these rare observations and has the power to prevent detrimental events, such as financial fraud, network intrusion, and social spam. However, conventional anomaly detection methods cannot handle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.) (Ma X, Wu J, Xue S, Yang J, Zhou C, Sheng QZ, Xiong H, Akoglu L. IEEE Trans Knowl Data Eng, 2021 [1]). Thanks to the rise of deep learning in solving these limitations, graph anomaly detection with deep learning has obtained an increasing attention from many scientists recently. However, while deep learning can capture unseen patterns of multi-dimensional Euclidean data, there is a huge number of applications where data are represented in the form of graphs. Graphs have been used to represent the structural relational information, which raises the graph anomaly detection problem—identifying anomalous graph objects (i.e., vertex, edges, sub-graphs, and change detection). These graphs can be constructed as a static graph, or a dynamic graph based on the availability of timestamp. Recent years have observed a huge efforts on static graphs, among which Graph Convolutional Network (GCN) has appeared as a useful class of models. A challenge today is to detect anomalies with dynamic structures. In this chapter, we aim at providing methods used for detecting anomalies in static and dynamic graphs using graph analysis, graph embedding, and graph convolutional neural networks. For static graphs we categorize these methods according to plain and attribute static graphs. For dynamic graphs we categorize existing methods according to the type of anomalies that they can detect. Moreover, we focus on the challenges in this research area and discuss the strengths and weaknesses of various methods in each category. Finally, we provide open challenges for graph anomaly detection using graph convolutional neural networks on dynamic graphs.

These authors contributed equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q.Z., Xiong, H., Akoglu, L.: A comprehensive survey on graph anomaly detection with deep learning. IEEE Trans. Knowl. Data Eng. (2021)

    Google Scholar 

  2. Shen Wang, P.S.Y.: Chapter 26 Graph neural networks in anomaly detection, pp. 558–578. https://graph-neural-networks.github.io/static/file/chapter26.pdf

  3. Xu, M.: Understanding graph embedding methods and their applications. arXiv:2012.08019 (2020)

  4. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4–24 (2020)

    Article  MathSciNet  Google Scholar 

  5. Zhou, R., Zhang, Q., Zhang, P., Niu, L., Lin, X.: Anomaly detection in dynamic attributed networks. Neural Comput. Appl. 33(6), 2125–2136 (2021)

    Article  Google Scholar 

  6. Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: extending highdimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30(3), 83–98 (2013)

    Article  Google Scholar 

  7. Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 922–929 (2019)

    Google Scholar 

  8. Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., Samatova, N.F.: Anomaly detection in dynamic networks: a survey. Wiley Interdiscip. Rev. Comput. Stat. 7(3), 223–247 (2015)

    Article  MathSciNet  Google Scholar 

  9. Li, Q., Han, Z., Wu, X.-M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  10. Gupta, A., Matta, P., Pant, B.: Graph neural network: current state of art, challenges and applications. Mater. Today Proc. (2021)

    Google Scholar 

  11. Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Disc. 29(3), 626–688 (2015)

    Article  MathSciNet  Google Scholar 

  12. Wang, H., Wu, J., Hu, W., Wu, X.: Detecting and assessing anomalous evolutionary behaviors of nodes in evolving social networks. ACM Trans. Knowl. Discov. Data (TKDD) 13(1), 1–24 (2019)

    Article  Google Scholar 

  13. Zheng, L., Li, Z., Li, J., Li, Z., Gao, J.: Addgraph: anomaly detection in dynamic graph using attention-based temporal GCN. IJCAI, 4419–4425 (2019)

    Google Scholar 

  14. Chen, H., Yin, H., Sun, X., Chen, T., Gabrys, B., Musial, K.: Multi-level graph convolutional networks for cross-platform anchor link prediction. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1503–1511 (2020)

    Google Scholar 

  15. Silva, J., Willett, R.: Hypergraph-based anomaly detection of highdimensional co-occurrences. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 563–569 (2008)

    Article  Google Scholar 

  16. Li, G., Jung, J.J.: Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series. Sci. Rep. 11(1), 1–10 (2021)

    Article  Google Scholar 

  17. Akoglu, L., McGlohon, M., Faloutsos, C.: Oddball: spotting anomalies in weighted graphs. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 410–421. Springer (2010)

    Google Scholar 

  18. Ding, Q., Katenka, N., Barford, P., Kolaczyk, E., Crovella, M.: Intrusion as (anti) social communication: characterization and detection. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 886–894 (2012)

    Google Scholar 

  19. 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)

    Google Scholar 

  20. Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)

    Google Scholar 

  21. Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017)

  22. Liu, N., Huang, X., Hu, X.: Accelerated local anomaly detection via resolving attributed networks. IJCAI, 2337–2343 (2017)

    Google Scholar 

  23. Wu, L., Hu, X., Morstatter, F., Liu, H.: Adaptive spammer detection with sparse group modeling. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 11 (2017)

    Google Scholar 

  24. Wang, Z., Lan, C.: Towards a hierarchical Bayesian model of multi-view anomaly detection. IJCAI, 2420–2426 (2020)

    Google Scholar 

  25. Miao, K., Shi, X., Zhang, W.-A.: Attack signal estimation for intrusion detection in industrial control system. Comput. Secur. 96, 101926 (2020)

    Google Scholar 

  26. Jie, F., Wang, C., Chen, F., Li, L., Wu, X.: Block-structured optimization for anomalous pattern detection in interdependent networks. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 1138–1143. IEEE (2019)

    Google Scholar 

  27. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016)

  28. 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)

    Google Scholar 

  29. Bandyopadhyay, S., Vivek, S.V., Murty, M.: Outlier resistant unsupervised deep architectures for attributed network embedding. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 25–33 (2020)

    Google Scholar 

  30. Li, Y., Huang, X., Li, J., Du, M., Zou, N.: Specae: spectral autoencoder for anomaly detection in attributed networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2233–2236 (2019)

    Google Scholar 

  31. Wang, J., Wen, R., Wu, C., Huang, Y., Xion, J.: Fdgars: Fraudster detection via graph convolutional networks in online app review system. In: Companion Proceedings of the 2019 World Wide Web Conference, pp. 310–316 (2019)

    Google Scholar 

  32. Zhang, S., Yin, H., Chen, T., Hung, Q.V.N., Huang, Z., Cui, L.: GCN-based user representation learning for unifying robust recommendation and fraudster detection. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 689–698 (2020)

    Google Scholar 

  33. Ji, T., Yang, D., Gao, J.: Incremental local evolutionary outlier detection for dynamic social networks. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 1–15. Springer (2013)

    Google Scholar 

  34. Saligrama, V., Chen, Z.: Video anomaly detection based on local statistical aggregates. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2112–2119. IEEE (2012)

    Google Scholar 

  35. Tran, L., Navasca, C., Luo, J.: Video detection anomaly via low-rank and sparse decompositions. In: 2012 Western New York Image Processing Workshop, pp. 17–20. IEEE (2012)

    Google Scholar 

  36. Duan, D., Li, Y., Jin, Y., Lu, Z.: Community mining on dynamic weighted directed graphs. In: Proceedings of the 1st ACM International Workshop on Complex Networks Meet Information & Knowledge Management, pp. 11–18 (2009)

    Google Scholar 

  37. Tantipathananandh, C., Berger-Wolf, T.Y.: Finding communities in dynamic social networks. In: 2011 IEEE 11th International Conference on Data Mining, pp. 1236–1241. IEEE (2011)

    Google Scholar 

  38. Chen, Z., Hendrix, W., Guan, H., Tetteh, I.K., Choudhary, A., Semazzi, F., Samatova, N.F.: Discovery of extreme events-related communities in contrasting groups of physical system networks. Data Min. Knowl. Disc. 27(2), 225–258 (2013)

    Article  MathSciNet  Google Scholar 

  39. Chen, Z., Hendrix, W., Samatova, N.F.: Community-based anomaly detection in evolutionary networks. J. Intell. Inf. Syst. 39(1), 59–85 (2012)

    Article  Google Scholar 

  40. Araujo, M., Papadimitriou, S., Gu¨nnemann, S., Faloutsos, C., Basu, P., Swami, A., Papalexakis, E.E., Koutra, D.: Com2: fast automatic discovery of temporal (‘comet’) communities. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 271–283. Springer (2014)

    Google Scholar 

  41. Zhang, Z., Cui, P., Pei, J., Wang, X., Zhu, W.: Timers: error-bounded SVD restart on dynamic networks. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  42. Du, L., Wang, Y., Song, G., Lu, Z., Wang, J.: Dynamic network embedding: an extended approach for skip-gram based network embedding. IJCAI 2018, 2086–2092 (2018)

    Google Scholar 

  43. Li, J., Dani, H., Hu, X., Tang, J., Chang, Y., Liu, H.: Attributed network embedding for learning in a dynamic environment. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 387–396 (2017)

    Google Scholar 

  44. Trivedi, R., Farajtabar, M., Biswal, P., Zha, H.: Dyrep: learning representations over dynamic graphs. In: International Conference on Learning Representations (2019)

    Google Scholar 

  45. Hou, C., Zhang, H., Tang, K., He, S.: DynWalks: global topology and recent changes awareness dynamic network embedding. arXiv:1907.11968 (2019)

  46. Cui, Z., Li, Z., Wu, S., Zhang, X., Liu, Q., Wang, L., Ai, M.: DYGCN: Dynamic graph embedding with graph convolutional network arXiv:2104.02962 (2021)

  47. Yu, W., Cheng, W., Aggarwal, C.C., Zhang, K., Chen, H., Wang, W.: Netwalk: a flexible deep embedding approach for anomaly detection in dynamic networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2672–2681 (2018)

    Google Scholar 

  48. Zhang, Y., Tangwongsan, K., Tirthapura, S.: Streaming k-means clustering with fast queries. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 449–460. IEEE (2017)

    Google Scholar 

  49. Liu, Z., Dou, Y., Yu, P.S., Deng, Y., Peng, H.: Alleviating the inconsistency problem of applying graph neural network to fraud detection. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1569–1572 (2020)

    Google Scholar 

  50. Zheng, P., Yuan, S., Wu, X., Li, J., Lu, A.: One-class adversarial nets for fraud detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1286–1293 (2019)

    Google Scholar 

  51. Seo, Y., Defferrard, M., Vandergheynst, P., Bresson, X.: Structured sequence modeling with graph convolutional recurrent networks. In: International Conference on Neural Information Processing, pp. 362–373. Springer (2018)

    Google Scholar 

  52. Manessi, F., Rozza, A., Manzo, M.: Dynamic graph convolutional networks. Pattern Recognit. 97, 107000 (2020)

    Google Scholar 

  53. Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv:1810.00826 (2018)

  54. Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T., Leiserson, C.: Evolvegcn: Evolving graph convolutional networks for dynamic graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5363–5370 (2020)

    Google Scholar 

  55. Duan, D., Tong, L., Li, Y., Lu, J., Shi, L., Zhang, C.: AANE: Anomaly aware network embedding for anomalous link detection. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 1002–1007. IEEE (2020)

    Google Scholar 

  56. Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D., Chen, H.: Structural temporal graph neural networks for anomaly detection in dynamic graphs. arXiv:2005.07427 (2020)

Download references

Author information

Authors and Affiliations

  1. ICube (Laboratoire des sciences de l’ingénieur, de l’informatique et de l’imagerie), UMR 7357, Université de Strasbourg, CNRS, 67000, Strasbourg, France

    Amani Abou Rida, Rabih Amhaz & Pierre Parrend

  2. ECAM Strasbourg-Europe, 67300, Schiltigheim, France

    Rabih Amhaz

  3. EPITA, 5, Rue Gustave Adolphe Hirn, 67000, Strasbourg, France

    Pierre Parrend

Authors
  1. Amani Abou Rida
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rabih Amhaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierre Parrend
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amani Abou Rida .

Editor information

Editors and Affiliations

  1. State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Nadia Nedjah

  2. Mathematics and Computer Science Department, Menoufia University, Faculty of Science, Menofia, Egypt

    Ahmed A. Abd El-Latif

  3. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Brij B. Gupta

  4. State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Luiza M. Mourelle

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rida, A.A., Amhaz, R., Parrend, P. (2022). Anomaly Detection on Static and Dynamic Graphs Using Graph Convolutional Neural Networks. In: Nedjah, N., Abd El-Latif, A.A., Gupta, B.B., Mourelle, L.M. (eds) Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities. Studies in Computational Intelligence, vol 1030. Springer, Cham. https://doi.org/10.1007/978-3-030-96737-6_12

Download citation

Publish with us

Policies and ethics

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy