Abstract
Dynamic networks raise new challenges for knowledge discovery. To efficiently handle this kind of data, analysis methods have to decompose the network, modelled by a graph, into similar sets of nodes. In this article, we present a graph decomposition algorithm that generates overlapping clusters. The complexity of this algorithm is \(O(|E| \cdot deg^2_{max} + |V| \cdot log(|V|))\). This algorithm is particularly efficient because it can detect major changes in the data as it evolves over time.
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Bourqui, R., Simonetto, P., Jourdan, F. (2010). A Stable Decomposition Algorithm for Dynamic Social Network Analysis. In: Guillet, F., Ritschard, G., Zighed, D.A., Briand, H. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00580-0_10
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DOI: https://doi.org/10.1007/978-3-642-00580-0_10
Publisher Name: Springer, Berlin, Heidelberg
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