Unsupervised Graph Anomaly Detection Algorithms Implemented in Apache Spark
- Авторлар: Semenov A.1, Mazeev A.1, Doropheev D.2, Yusubaliev T.3
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Мекемелер:
- Scientific Research Centre for Electronic Computer Technology (NICEVT) JSC
- Moscow Institute of Physics and Technology (State University)
- Quality Software Solutions Ltd.
- Шығарылым: Том 39, № 9 (2018)
- Беттер: 1262-1269
- Бөлім: 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
Дәйексөз келтіру
Аннотация
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.
Негізгі сөздер
Авторлар туралы
A. Semenov
Scientific Research Centre for Electronic Computer Technology (NICEVT) JSC
Хат алмасуға жауапты Автор.
Email: semenov@nicevt.ru
Ресей, Varshavskoe sh. 125, Moscow, 117587
A. Mazeev
Scientific Research Centre for Electronic Computer Technology (NICEVT) JSC
Email: semenov@nicevt.ru
Ресей, Varshavskoe sh. 125, Moscow, 117587
D. Doropheev
Moscow Institute of Physics and Technology (State University)
Email: semenov@nicevt.ru
Ресей, Institutskii per. 9, Dolgoprudny, Moscow oblast, 141701
T. Yusubaliev
Quality Software Solutions Ltd.
Email: semenov@nicevt.ru
Ресей, Moscow