This project provides a set of algorithms and tools to perform anomaly detection on complex networks. The project is experimental in nature and stability is not one of its main goals at the moment.
- scikit-feature
- numpy
- python-louvain
The algoritms implemented here are explained in the following works:
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Prado-Romero M.A., Oliva A.F., Hernández L.G. (2018) Identifying Twitter Users Influence and Open Mindedness Using Anomaly Detection. In: Hernández Heredia Y., Milián Núñez V., Ruiz Shulcloper J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science, vol 11047. Springer, Cham.
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Prado-Romero M.A., Gago-Alonso A. Detecting contextual collective anomalies at a glance. In 2016 23rd International Conference on Pattern Recognition (ICPR) 2016 Dec 4 (pp. 2532-2537). IEEE.
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Helling TJ, Scholtes JC, Takes FW. A community-aware approach for identifying node anomalies in complex networks. InInternational Conference on Complex Networks and their Applications 2018 Dec 11 (pp. 244-255). Springer, Cham.