Abstract
Finding communities in networks is a hot topic in several research areas like social network, graph theory or sociology among others. This work considers the community finding problem as a clustering problem where an evolutionary approach can provide a new method to find overlapping and stable communities in a graph. We apply some clustering concepts to search for new solutions that use new simple fitness functions which combine network properties with the clustering coefficient of the graph. Finally, our approach has been applied to the Eurovision contest dataset, a well-known social-based data network, to show how communities can be found using our method.
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Bello, G., Menéndez, H., Camacho, D. (2011). Using the Clustering Coefficient to Guide a Genetic-Based Communities Finding Algorithm. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_20
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DOI: https://doi.org/10.1007/978-3-642-23878-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23877-2
Online ISBN: 978-3-642-23878-9
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