Abstract
In this paper, we propose a method allowing decomposition of directed networks into cores, which final objective is the detection of communities.We based our approach on the fact that a community should be composed of elements having communication in both directions. Therefore, we propose a method based on digraph kernelization and strongly p-connected components. By identifying cores, one can use based-centers clustering methods to generate full communities. Some experiments have been made on three real-world networks, and have been evaluated using the V-Measure, having a more precise analysis through its two sub-measures: homogeneity and completeness. Our work proposes different directions about the use of kernelization into structure analysis, and strong connectivity concept as an alternative to modularity optimization.
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Keywords
- Community Detection
- Normalize Mutual Information
- Directed Network
- Strongly Connect Component
- Strong Connectivity
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Levorato, V. (2014). Core Decomposition in Directed Networks: Kernelization and Strong Connectivity. In: Contucci, P., Menezes, R., Omicini, A., Poncela-Casasnovas, J. (eds) Complex Networks V. Studies in Computational Intelligence, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-05401-8_13
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DOI: https://doi.org/10.1007/978-3-319-05401-8_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05400-1
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