Abstract
Starting from the main idea of Symbolic Data Analysis to extend Statistics and Data Mining methods from first-order to second-order objects, we focus on network data—as defined in the framework of Social Network Analysis—to define a graph structure and the underlying network in the context of complex data objects. A Network Symbolic description is defined according to the statistical characterization of the network topological properties. We use suitable network measures, which are represented by means of symbolic variables. Their study through multidimensional data analysis, allows for the synthetic representation of a network as a point onto a metric space. The proposed approach is discussed on the basis of a simulation study considering three classical network growth processes.
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Simulations and network statistics are obtained by: R version 2.15.2 (2012-10-26). Base packages: base, datasets, graphics, grDevices, methods, stats, utils; other: igraph 0.6.5-1, sna 2.2-1.
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Acknowledgements
This work is financed by the ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT—Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project “FCOMP-01-0124-FEDER-037281”.
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Giordano, G., Brito, P. (2014). Social Networks as Symbolic Data. In: Vicari, D., Okada, A., Ragozini, G., Weihs, C. (eds) Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-06692-9_15
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DOI: https://doi.org/10.1007/978-3-319-06692-9_15
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