Mining Anomalies in Graph Data

  • N. N. R. Ranga SuriEmail author
  • Narasimha Murty M
  • G. Athithan
Part of the Intelligent Systems Reference Library book series (ISRL, volume 155)


Mining graph data is an important data mining task due to its significance in network analysis and several other contemporary applications. With this backdrop, this chapter explores the potential applications of outlier detection principles in graph/network data mining for anomaly detection. One of the focus areas is to detect arbitrary subgraphs of the input graph exhibiting deviating characteristics. In this direction, graph mining methods developed based on latest algorithmic techniques for detecting various kinds of anomalous subgraphs are explored here. It also includes an experimental study involving benchmark graph data sets to demonstrate the process of anomaly detection in network/graph data.


  1. 1.
    Aggarwal, C.C., Wang, H. (eds.): Managing and Mining Graph Data. Advances in Database Systems, vol. 40. Springer (2010)Google Scholar
  2. 2.
    Akoglu, L., McGlohon, M., Faloutsos, C.: Oddball: Spotting anomalies in weighted graphs. In: 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD), pp. 410–421. Hyderabad, India (2010)CrossRefGoogle Scholar
  3. 3.
    Amaral, A.A., Mendes, L.S., Pena, E.H.M., Zarpelão, B.B., Jr., Proença, M.L.: Network anomaly detection by ip flow graph analysis: a ddos attack case study. In: 32nd International Conference of the Chilean Computer Science Society. SCCC, pp. 90–94. IEEE Computer Society, Temuco, Cautin, Chile (2013)Google Scholar
  4. 4.
    Chakrabarti, D.: Autopart: Parameter-free graph partitioning and outlier detection. In: PKDD, pp. 112–124 (2004)Google Scholar
  5. 5.
    Cook, D.J., Holder, L.B.: Mining Graph Data. Wiley, USA (2006)CrossRefGoogle Scholar
  6. 6.
    Dalmia, A., Gupta, M., Varma, V.: Query-based evolutionary graph cuboid outlier detection. In: 16th International Conference on Data Mining Workshops (ICDMW), pp. 85–92. IEEE Computer Society, Barcelona, Spain (2016)Google Scholar
  7. 7.
    Dourisboure, Y., Geraci, F., Pellegrini, M.: Extraction and classification of dense communities in the web. In: WWW, pp. 461–470. Banff, Alberta, Canada (2007)Google Scholar
  8. 8.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gupta, M., Mallya, A., Roy, S., Cho, J.H.D., Han, J.: Local learning for mining outlier subgraphs from network datasets. In: SDM (2014)Google Scholar
  10. 10.
    Harshman, R.A.: Foundations of the parafac procedure: models and conditions for an explanatory multimodal factor analysis (1970)Google Scholar
  11. 11.
    Hooi, B., Shin, K., Song, H.A., Beutel, A., Shah, N.: Graph-based fraud detection in the face of camouflage. ACM Trans. Knowl. Discov. Data 11(4), Article ID 44, 1–26 (2017)CrossRefGoogle Scholar
  12. 12.
    Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Detecting suspicious following behavior in multimillion-node social networks. In: WWW (Companion). ACM (2014)Google Scholar
  13. 13.
    Koutra, D., Faloutsos, C.: Individual and Collective Graph Mining: Principles, Algorithms, and Applications. Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool Publishers (2017)Google Scholar
  14. 14.
    Krishna, V., Suri, N.N.R.R., Athithan, G.: Mugram: a multi-labelled graph matching. In: International Conference on Recent Advances in Computing and Software Systems, pp. 19–26. IEEE Xplore, Chennai, India (2012)Google Scholar
  15. 15.
    Krishna, V., Suri, N.N.R.R., Athithan, G.: A comparative survey of algorithms for frequent subgraph discovery. Curr. Sci. 100(2), 190–198 (2011)Google Scholar
  16. 16.
    Lad, M., Massey, D., Zhang, L.: Visualizing internet routing changes. IEEE Trans. Vis. Comput. Grapics 12(6), 1450–1460 (2006)CrossRefGoogle Scholar
  17. 17.
    Lee, V.E., Ruan, N., Jin, R., Aggarwal, C.: A survey of algorithms for dense subgraph discovery. In: Managing and Mining Graph Data, pp. 303–336. Springer (2010)Google Scholar
  18. 18.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature (1999)Google Scholar
  19. 19.
    Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection (2014).
  20. 20.
    Leskovec, J.: Large-scale graph representation learning. In: IEEE International Conference on Big Data, p. 4. Boston, MA, USA (2017)Google Scholar
  21. 21.
    Li, N., Sun, H., Chipman, K., George, J., Yan, X.: A probablistic approach to uncovering attributed graph anomalies. In: SDM (2014)Google Scholar
  22. 22.
    McAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 548–556. Nevada, USA (2012)Google Scholar
  23. 23.
    Noble, C.C., Cook, D.J.: Graph-based anomaly detection. In: SIGKDD, pp. 631–636. Washington, DC, USA (2003)Google Scholar
  24. 24.
    Papadopoulos, S., Moustakas, K., Drosou, A., Tzovaras, D.: Border gateway protocol graph: detecting and visualizing internet routing anomalies. IET Inf. Secur. 10(3), 125–133 (2016)CrossRefGoogle Scholar
  25. 25.
    Papalexakis, E., Pelechrinis, K., Faloutsos, C.: Spotting misbehaviors in location-based social networks using tensors. In: WWW(Companion). ACM (2014)Google Scholar
  26. 26.
    Rattigan, M.J., Jensen, D.: The case for anomalous link discovery. SIGKDD Explor. 7(2), 41–47 (2006)CrossRefGoogle Scholar
  27. 27.
    Routing information service project (RIS).
  28. 28.
    Sariyuce, A.E., Seshadhri, C., Pinar, A., Catalyurek, U.V.: Finding the heirarchy of dense subgraphs using nucleus decompositions. In: WWW, pp. 927–937. ACM, Florence, Italy (2015)Google Scholar
  29. 29.
    Shin, K., Eliassi-Rad, T., Faloutsos, C.: Patterns and anomalies in k-cores of real-world graphs with applications. Knowl. Inf. Syst. 54(3), 677–710 (2018)CrossRefGoogle Scholar
  30. 30.
    Snasel, V., Horak, Z., Kocibova, J., Abraham, A.: Reducing social network dimensions using matrix factorization methods. In: Advances in Social Network Analysis and Mining, pp. 348–351. Athens, Greece (2009)Google Scholar
  31. 31.
    Suri, N.N.R.R., Krishna, V., Kumar, K.R.P., Rakshit, S.: Detecting hotspots in network data based on spectral graph theory. In: Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 45–50. IEEE Xplore (2016)Google Scholar
  32. 32.
    Suri, N.N.R.R., Murty, M.N., Athithan, G.: Mining anomalous sub-graphs in graph data using non-negative matrix factorization. In: P. Maji (ed.) 5th International Conference on Pattern Recognition and Machine Intelligence (PReMI), LNCS, vol. 8251, pp. 88–93. Springer, Berlin, Heidelberg (2013)Google Scholar
  33. 33.
    Swarnkar, T., Mitra, P.: Graph based unsupervised feature selection for microarray data. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Workshops, pp. 750–751. Philadelphia, USA (2012)Google Scholar
  34. 34.
    Vural, M., Jung, P., Stanczak, S.: A new outlier detection method based on anti-sparse representations. In: 25th Signal Processing and Communications Applications Conference. SIU, pp. 1–4. IEEE, Antalya, Turkey (2017)Google Scholar
  35. 35.
    Wang, F., Li, T., Wang, X., Zhu, S., Ding, C.: Community discovery using nonnegative matrix factorization. Data Min. Knowl. Discov. 22(3), 493–521 (2011)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Zhang, L., Wang, H., Li, C., Shao, Y., Ye, Q.: Unsupervised anomaly detection algorithm of graph data based on graph kernel. In: 4th International Conference on Cyber Security and Cloud Computing. CSCloud, pp. 58–63. IEEE, New York, NY, USA (2017)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • N. N. R. Ranga Suri
    • 1
    Email author
  • Narasimha Murty M
    • 2
  • G. Athithan
    • 3
  1. 1.Centre for Artificial Intelligence and Robotics (CAIR)BangaloreIndia
  2. 2.Department of Computer Science and AutomationIndian Institute of Science (IISc)BangaloreIndia
  3. 3.Defence Research and Development Organization (DRDO)New DelhiIndia

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