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CADA

Community-aware detection of anomalies [1]

This is the code for detecting node anomalies in networks with NetworkX by including community structure from two out-of-the-box community detection algorithm: (1) Louvain [1], and (2) Infomap [2]. The algorithm is described here.

For (1), Python package Python-Louvain is used.

For (2), Python package Infomap is used.

[1] Helling, T.J., Scholtes, J. C., Takes, F.W. A community-aware approach for identifying node anomalies in complex networks. In Proceedings of the 7th International Conference on Complex Networks, CI, pages 244–255. Springer, 2019.

[2] Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment10008(10), 6 (2008)

[3] Rosvall, M., Bergstrom, C.: Maps of random walks on complex networks reveal community structure. Proceedings of National Academy of Sciences,105(4), 1118–1123 (2008)

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