Abstract
Complex networks are generally composed of dense sub-networks called communities. Many algorithms have been proposed to automatically detect such communities. However, they are often unstable and behave nondeterministically. We propose here to use this non-determinism in order to compute groups of nodes on which community detection algorithms agree most of the time.We show that these groups of nodes, called community cores, are more similar to Ground Truth than communities in real and artificial networks. Furthermore, we show that in contrary to the classical approaches, we can reveal the absence of community structure in random graphs.
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Seifi, M., Junier, I., Rouquier, JB., Iskrov, S., Guillaume, JL. (2013). Stable Community Cores in Complex Networks. In: Menezes, R., Evsukoff, A., González, M. (eds) Complex Networks. Studies in Computational Intelligence, vol 424. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30287-9_10
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DOI: https://doi.org/10.1007/978-3-642-30287-9_10
Publisher Name: Springer, Berlin, Heidelberg
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