Unsupervised Graph Anomaly Detection Algorithms Implemented in Apache Spark
- Autores: Semenov A.1, Mazeev A.1, Doropheev D.2, Yusubaliev T.3
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Afiliações:
- Scientific Research Centre for Electronic Computer Technology (NICEVT) JSC
- Moscow Institute of Physics and Technology (State University)
- Quality Software Solutions Ltd.
- Edição: Volume 39, Nº 9 (2018)
- Páginas: 1262-1269
- Seção: 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
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Resumo
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.
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Sobre autores
A. Semenov
Scientific Research Centre for Electronic Computer Technology (NICEVT) JSC
Autor responsável pela correspondência
Email: semenov@nicevt.ru
Rússia, Varshavskoe sh. 125, Moscow, 117587
A. Mazeev
Scientific Research Centre for Electronic Computer Technology (NICEVT) JSC
Email: semenov@nicevt.ru
Rússia, Varshavskoe sh. 125, Moscow, 117587
D. Doropheev
Moscow Institute of Physics and Technology (State University)
Email: semenov@nicevt.ru
Rússia, Institutskii per. 9, Dolgoprudny, Moscow oblast, 141701
T. Yusubaliev
Quality Software Solutions Ltd.
Email: semenov@nicevt.ru
Rússia, Moscow