Efficient Outlier Detection in Hyperedge Streams Using MinHash and Locality-Sensitive Hashing

  • Stephen Ranshous
  • Mandar Chaudhary
  • Nagiza F. SamatovaEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)


Mining outliers in graph data is a rapidly growing area of research. Traditional methods focus either on static graphs, or restrict relationships to be pairwise. In this work we address both of these limitations directly, and propose the first approach for mining outliers in hyperedge streams. Hyperedges, which generalize edges, faithfully capture higher order relationships that naturally occur in complex systems. Our model annotates every incoming hyperedge with an outlier score, which is based on the incident vertices and the historical relationships among them. Additionally, we describe an approximation scheme that ensures our model is suitable for being run in streaming environments. Experimental results on several real-world datasets show our model effectively identifies outliers, and that our approximation provides speedups between 33–775x.



This material is based on work supported in part by the Department of Energy National Nuclear Security Administration under Award Number(s) DE-NA0002576, NSF grant 1029711, the DOE SDAVI Institute, and the U.S. National Science Foundation (Expeditions in Computing program).


  1. 1.
    Enron network dataset—KONECT. May 2015
  2. 2.
    Akoglu, L., McGlohon, M., Faloutsos, C.: Oddball: Spotting anomalies in weighted graphs. In: Advances in Knowledge Discovery and Data Mining, pp. 410–421. Springer (2010)Google Scholar
  3. 3.
    Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Disc. 29(3), 626–688 (2014)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Disc. 29(3), 626–688 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Broder, A.: Identifying and filtering near-duplicate documents. In: Combinatorial Pattern Matching, pp. 1–10. Springer (2000)Google Scholar
  6. 6.
    Broder, A.Z.: On the resemblance and containment of documents. In: Compression and Complexity of Sequences 1997. Proceedings, pp. 21–29. IEEE (1997)Google Scholar
  7. 7.
    Broder, A.Z., Glassman, S.C., Manasse, M.S., Zweig, G.: Syntactic clustering of the web. Comput. Netw. ISDN Syst. 29(8), 1157–1166 (1997)CrossRefGoogle Scholar
  8. 8.
    El-Yaniv, R., Nisenson, M.: Optimal single-class classification strategies. In: Advances in Neural Information Processing Systems, pp. 377–384 (2007)Google Scholar
  9. 9.
    Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)CrossRefzbMATHGoogle Scholar
  10. 10.
    Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge university press (2014)Google Scholar
  11. 11.
    Park, Y., Priebe, C., Marchette, D., Youssef, A.: Anomaly detection using scan statistics on time series hypergraphs. In: Link Analysis, Counterterrorism and Security (LACTS) Conference, p. 9 (2009)Google Scholar
  12. 12.
    Priebe, C.E., Conroy, J.M., Marchette, D.J., Park, Y.: Scan statistics on enron graphs. Comput. Math. Organ. Theory 11(3), 229–247 (2005)CrossRefzbMATHGoogle Scholar
  13. 13.
    Ranshous, S., Harenberg, S., Sharma, K., Samatova, N.F.: A scalable approach for outlier detection in edge streams using sketch-based approximations. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 189–197. SIAM (2016)Google Scholar
  14. 14.
    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)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Scott, C., Kolaczyk, E.: Nonparametric assessment of contamination in multivariate data using generalized quantile sets and fdr. J. Comput. Graph. Stat. 19(2), 439–456 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Silva, J., Willett, R.: Hypergraph-based anomaly detection of high-dimensional co-occurrences. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 563–569 (2009)CrossRefGoogle Scholar
  17. 17.
    Sun, J., Qu, H., Chakrabarti, D., Faloutsos, C.: Neighborhood formation and anomaly detection in bipartite graphs. In: Fifth IEEE International Conference on Data Mining (ICDM’05), p. 8. IEEE (2005)Google Scholar
  18. 18.
    Wei, L., Qian, W., Zhou, A., Jin, W., Jeffrey, X.Y.: Hot: Hypergraph-based outlier test for categorical data. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 399–410. Springer (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Stephen Ranshous
    • 1
  • Mandar Chaudhary
    • 1
  • Nagiza F. Samatova
    • 1
    Email author
  1. 1.NCSURaleighUSA

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