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2014 | OriginalPaper | Chapter

Social Networks as Symbolic Data

Authors : Giuseppe Giordano, Paula Brito

Published in: Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Publisher: Springer International Publishing

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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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previous chapter Influence Diagnostics for Meta-Analysis of Individual Patient Data Using Generalized Linear Mixed Models
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Footnotes
1
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.
 
Literature
Batagelj, V. (1988). Generalized Ward and related clustering problems. In H.-H. Bock (Ed.), Classification and related methods of data analysis (pp. 67–74). Amsterdam: North-Holland.
Bock, H. -H., & Diday, E. (2000). Analysis of symbolic data. Berlin: Springer.CrossRef
De Solla Price, D. J. (1976). A general theory of bibliometric and other cumulative advantage processes. Journal of the American Society for Information Science, 27(5), 292–306.CrossRef
Erdös, P., & Rényi, A. (1960). On the evolution of random graphs. Publication of the Mathematical Institute of the Hungarian Academy of Science, 5, 17–61
Freeman, L. C. (1979). Centrality in social networks I: Conceptual clarification. Social Networks, 1, 215–239.CrossRef
Irpino, A., & Verde, R. (2006). A new wasserstein based distance for the hierarchical clustering of histogram symbolic data. In V. Batagelj, et al. (Eds.), Proceedings of IFCS 2006 (pp. 185–192). Heidelberg: Springer.
Kolaczyk, E. D. (2009). Statistical analysis of network data. Methods and models. Springer Series in Statistics. New York: Springer.
Noirhomme-Fraiture, M., & Brito, P. (2011). Far beyond the classical data models: Symbolic data analysis. Statistical Analysis and Data Mining, 4(2), 157–170.CrossRefMathSciNet
Wasserman, S., & Faust, K. (1994). Social networks analysis: Methods and applications. New York: Cambridge University Press.CrossRef
Metadata
Title
Social Networks as Symbolic Data
Authors
Giuseppe Giordano
Paula Brito
Copyright Year
2014
Book
Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Print ISBN: 978-3-319-06691-2

Electronic ISBN: 978-3-319-06692-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-06692-9_15

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