Abstract
A suitable state model can be retrieved from a Karhunen-Loeve Transformation to build a new decision process from which, we can extract useful knowledge and information about the identified underlying sub-communities from an initial network. The aim of this method is to build a framework for a multi-level knowledge retrieval. So, besides the capacity of this methodology to reduce the high dimensionality of the data, the new detection scheme is able to extract, from the sub-communities, the most relevant nodes and the dense sub-groups with the definition and formulation of new quantities related to the notions of energy and co-energy. The energy of a node is the rate of its participation on a the whole set of activities while the notion of co-energy defines the rate of interaction/link between two nodes. These two important features are used to make each link weighted and bounded, so that we will be able to perform a thorough refinement of the sub-community discovery. This study allows to perform a multi-level analysis by extracting information either per-link or per-intra-sub-community. This methodology is applied to a real world dataset where the workload of activities over a set of events is considered.
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Ndong, J., Gueye, I. (2017). A New Decision Technique For Sub-community And Multi-Level Knowledge Extraction In Social Networks. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_21
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DOI: https://doi.org/10.1007/978-3-319-50901-3_21
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