Unsupervised Graph Anomaly Detection Algorithms Implemented in Apache Spark
- 作者: Semenov A.1, Mazeev A.1, Doropheev D.2, Yusubaliev T.3
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隶属关系:
- Scientific Research Centre for Electronic Computer Technology (NICEVT) JSC
- Moscow Institute of Physics and Technology (State University)
- Quality Software Solutions Ltd.
- 期: 卷 39, 编号 9 (2018)
- 页面: 1262-1269
- 栏目: Part 1. Special issue “High Performance Data Intensive Computing” Editors: V. V. Voevodin, A. S. Simonov, and A. V. Lapin
- URL: https://journals.rcsi.science/1995-0802/article/view/203172
- DOI: https://doi.org/10.1134/S1995080218090184
- ID: 203172
如何引用文章
详细
The graph anomaly detection problem occurs in many application areas and can be solved by spotting outliers in unstructured collections of multi-dimensional data points, which can be obtained by graph analysis algorithms. We implement the algorithm for the small community analysis and the approximate LOF algorithm based on Locality-Sensitive Hashing, apply the algorithms to a real world graph and evaluate scalability of the algorithms. We use Apache Spark as one of the most popular Big Data frameworks.
作者简介
A. Semenov
Scientific Research Centre for Electronic Computer Technology (NICEVT) JSC
编辑信件的主要联系方式.
Email: semenov@nicevt.ru
俄罗斯联邦, Varshavskoe sh. 125, Moscow, 117587
A. Mazeev
Scientific Research Centre for Electronic Computer Technology (NICEVT) JSC
Email: semenov@nicevt.ru
俄罗斯联邦, Varshavskoe sh. 125, Moscow, 117587
D. Doropheev
Moscow Institute of Physics and Technology (State University)
Email: semenov@nicevt.ru
俄罗斯联邦, Institutskii per. 9, Dolgoprudny, Moscow oblast, 141701
T. Yusubaliev
Quality Software Solutions Ltd.
Email: semenov@nicevt.ru
俄罗斯联邦, Moscow