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On the influence of nodes' characteristic in inter-organisational innovation networks structure

On the influence of nodes' characteristic in inter-organisational innovation networks structure representation of inter-relations, where the network structure indicates the mapping of connections among elements. Here, complex networks are a set of des N that are associated by links M , representing network elements and the interactions among them respectively. We aim that a suitable model to represent invation network topology among the different network organations the scale-fr model (Barabási and Albert, 1999). Scale-fr networks are open and dynamically formed by continuous addition of new des that represent members, while links among members mimic collaborative agrments.1 Although research focused mainly on the network's structure, in many systems the des themselves have peculiar charactertics, which carry significant information regarding their role in the topology of the network. Indd, in several systems, adjacent des show relevant correlations in their features meaning that they have a higher chance to connect to each other (de Almeida et al., 2013; Skerlavaj et al., 2010). Thus, the number of links betwn des larger than expected if the charactertics are randomly dtributed due to the dyadic effect (Park and Barabási, 2007). Besides, if we consider the weights on the links, such values can be considered as measures of intensity, conceivably affected by the des' charactertic. Often, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computational Economics and Econometrics Inderscience Publishers

On the influence of nodes' characteristic in inter-organisational innovation networks structure

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Publisher
Inderscience Publishers
Copyright
Copyright © 2016 Inderscience Enterprises Ltd.
ISSN
1757-1170
eISSN
1757-1189
DOI
10.1504/IJCEE.2016.077247
Publisher site
See Article on Publisher Site

Abstract

representation of inter-relations, where the network structure indicates the mapping of connections among elements. Here, complex networks are a set of des N that are associated by links M , representing network elements and the interactions among them respectively. We aim that a suitable model to represent invation network topology among the different network organations the scale-fr model (Barabási and Albert, 1999). Scale-fr networks are open and dynamically formed by continuous addition of new des that represent members, while links among members mimic collaborative agrments.1 Although research focused mainly on the network's structure, in many systems the des themselves have peculiar charactertics, which carry significant information regarding their role in the topology of the network. Indd, in several systems, adjacent des show relevant correlations in their features meaning that they have a higher chance to connect to each other (de Almeida et al., 2013; Skerlavaj et al., 2010). Thus, the number of links betwn des larger than expected if the charactertics are randomly dtributed due to the dyadic effect (Park and Barabási, 2007). Besides, if we consider the weights on the links, such values can be considered as measures of intensity, conceivably affected by the des' charactertic. Often,

Journal

International Journal of Computational Economics and EconometricsInderscience Publishers

Published: Jan 1, 2016

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