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On Algebraic Graph Theory and the Dynamics of Innovation Networks


Battiston, Stefano; Napoletano, Mauro; Schweitzer, Frank; Michael D, König (2008). On Algebraic Graph Theory and the Dynamics of Innovation Networks. Networks and heterogeneous media, 3(2):201-219.

Abstract

We investigate some of the properties and extensions of a dynamic innovation network model recently introduced in. In the model, the set of efficient graphs ranges, depending on the cost for maintaining a link, from the complete graph to the (quasi-) star, varying within a well defined class of graphs. However, the interplay between dynamics on the nodes and topology of the network leads to equilibrium networks which are typically not efficient and are characterized, as observed in empirical studies of R&D networks, by sparseness, presence of clusters and heterogeneity of degree. In this paper, we analyze the relation between the growth rate of the knowledge stock of the agents from R&D collaborations and the properties of the adjacency matrix associated with the network of collaborations. By means of computer simulations we further investigate how the equilibrium network is affected by increasing the evaluation time τ over which agents evaluate whether to maintain a link or not. We show that only if τ is long enough, efficient networks can be obtained by the selfish link formation process of agents, otherwise the equilibrium network is inefficient. This work should assist in building a theoretical framework of R&D networks from which policies can be derived that aim at fostering efficient innovation networks.

Abstract

We investigate some of the properties and extensions of a dynamic innovation network model recently introduced in. In the model, the set of efficient graphs ranges, depending on the cost for maintaining a link, from the complete graph to the (quasi-) star, varying within a well defined class of graphs. However, the interplay between dynamics on the nodes and topology of the network leads to equilibrium networks which are typically not efficient and are characterized, as observed in empirical studies of R&D networks, by sparseness, presence of clusters and heterogeneity of degree. In this paper, we analyze the relation between the growth rate of the knowledge stock of the agents from R&D collaborations and the properties of the adjacency matrix associated with the network of collaborations. By means of computer simulations we further investigate how the equilibrium network is affected by increasing the evaluation time τ over which agents evaluate whether to maintain a link or not. We show that only if τ is long enough, efficient networks can be obtained by the selfish link formation process of agents, otherwise the equilibrium network is inefficient. This work should assist in building a theoretical framework of R&D networks from which policies can be derived that aim at fostering efficient innovation networks.

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Additional indexing

Item Type:Journal Article, not_refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Physical Sciences > General Engineering
Physical Sciences > Computer Science Applications
Physical Sciences > Applied Mathematics
Scope:Discipline-based scholarship (basic research)
Language:English
Date:2008
Deposited On:25 Aug 2023 08:59
Last Modified:26 Jun 2024 03:37
Publisher:AIMS Press
ISSN:1556-1801
OA Status:Hybrid
Publisher DOI:https://doi.org/10.3934/nhm.2008.3.201
Other Identification Number:merlin-id:10150
  • Content: Published Version
  • Language: English