Abstract

Dense subgraphs are fundamental patterns in graphs, and dense subgraph detection is often the key step of numerous graph mining applications. Most of the existing methods aim to find a single subgraph with a high density. However, dense subgraphs at different granularities could reveal more intriguing patterns in the underlying graph. In this paper, we propose to hierarchically detect dense subgraphs. The key idea of our method (HiDDen) is to envision the density of subgraphs as a relative measure to its background (i.e., the subgraph at the coarse granularity). Given that the hierarchical dense subgraph detection problem is essentially a nonconvex quadratic programming problem, we propose effective and efficient alternative projected gradient based algorithms to solve it. The experimental evaluations on real graphs demonstrate that (1) our proposed algorithms find subgraphs with an up to 40% higher density in almost every hierarchy; (2) the densities of different hierarchies exhibit a desirable variety across different granularities; (3) our projected gradient descent based algorithm scales linearly w.r.t the number of edges of the input graph; and (4) our methods are able to reveal interesting patterns in the underlying graphs (e.g., synthetic ID in financial fraud detection).

Original languageEnglish (US)
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
PublisherSociety for Industrial and Applied Mathematics Publications
Pages570-578
Number of pages9
ISBN (Electronic)9781611974874
StatePublished - 2017
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: Apr 27 2017Apr 29 2017

Other

Other17th SIAM International Conference on Data Mining, SDM 2017
CountryUnited States
CityHouston
Period4/27/174/29/17

Fingerprint

Quadratic programming

Keywords

  • Financial fraud detection
  • Graph mining
  • Hierarchical dense subgraph detection

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

Cite this

Zhang, S., Zhou, D., Yildirim, M. Y., Alcorn, S., He, J., Davulcu, H., & Tong, H. (2017). HiDDen: Hierarchical dense subgraph detection with application to financial fraud detection. In Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017 (pp. 570-578). Society for Industrial and Applied Mathematics Publications.

HiDDen : Hierarchical dense subgraph detection with application to financial fraud detection. / Zhang, Si; Zhou, Dawei; Yildirim, Mehmet Yigit; Alcorn, Scott; He, Jingrui; Davulcu, Hasan; Tong, Hanghang.

Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. Society for Industrial and Applied Mathematics Publications, 2017. p. 570-578.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhang, S, Zhou, D, Yildirim, MY, Alcorn, S, He, J, Davulcu, H & Tong, H 2017, HiDDen: Hierarchical dense subgraph detection with application to financial fraud detection. in Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. Society for Industrial and Applied Mathematics Publications, pp. 570-578, 17th SIAM International Conference on Data Mining, SDM 2017, Houston, United States, 4/27/17.
Zhang S, Zhou D, Yildirim MY, Alcorn S, He J, Davulcu H et al. HiDDen: Hierarchical dense subgraph detection with application to financial fraud detection. In Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. Society for Industrial and Applied Mathematics Publications. 2017. p. 570-578
Zhang, Si ; Zhou, Dawei ; Yildirim, Mehmet Yigit ; Alcorn, Scott ; He, Jingrui ; Davulcu, Hasan ; Tong, Hanghang. / HiDDen : Hierarchical dense subgraph detection with application to financial fraud detection. Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017. Society for Industrial and Applied Mathematics Publications, 2017. pp. 570-578
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