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Network Formation and Geography: Modelling Approaches, Underlying Conceptions, Recent and Promising Extensions

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The Geography of Networks and R&D Collaborations

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

Abstract

Due to the strong polarisation of economic activities in space and rise in collaborative behaviour, increasing attention has recently been devoted to the relationship between geography and network formation. The studies conducted on this topic reveal a high variation in terms of methodologies. Putting special emphasis on R&D networks, the aim of this chapter is to review the different methods and assess their ability to address the issues raised by the relationship between network and space. We first discuss the different facets of the relationship between geography and networks. Then, we detail the methodological approaches and their capability to test each effect of geography on network formation. We argue that the effect of distance on dyads have received the major attention so far, but the development of block modelling and top-down approaches opens new research perspectives on how distance or location might affect formation of more complex structures. Moreover, recent improvement in temporal models also offers opportunities to better separate spatial effects from that of influence over time.

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Notes

  1. 1.

    As shall be seen in the succeeding section, this utility either refers to a utility obtained out of a tie (see binary choice models), or to the utility out of the overall network (see ERGM).

  2. 2.

    However, if proximity is often associated with the tacit dimension of knowledge, we must avoid an overly simplistic view (Massard and Mehier 2009). There are probably complementarities between tacit and codified knowledge, any two being transmitted both locally and remotely. The link between proximity and knowledge can then lie in the way of combining the tacit and codified nature of knowledge.

  3. 3.

    These effects are similar to the “local configurations” in Exponential Random Graph Models that will be discussed in the sequel.

  4. 4.

    As shown by Park and Newman (2004) random graph models can be expressed as a constrained maximum entropy problem; which maximizes the entropy in the probability distribution of observing a particular network configuration. In the earlier random graph models (Erdös and Renyi 1959) the problem is constrained only by the number of edges in the network and a probability distribution which assigns the same probability to all networks with the same number of edges is obtained. However, in more recent models as shall be seen in subsection on Exponential Random Graph Models, the preferences of actors for homophily, central agents, closure, etc. can be used as additional constraints by defining local configurations accordingly.

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Acknowledgements

This research has received funding from the European Community’s Seventh Framework Programme under grant agreement no. 266834.

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Authors and Affiliations

  1. Université de Lyon, Lyon, F-69007, France

    Corinne Autant-Bernard & Çilem Selin Hazir

  2. Université Jean Monnet, Saint-Etienne, F-42000, France

    Corinne Autant-Bernard & Çilem Selin Hazir

  3. CNRS, GATE Lyon Saint-Etienne, Ecully, F-69130, France

    Corinne Autant-Bernard & Çilem Selin Hazir

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  1. Corinne Autant-Bernard
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  2. Çilem Selin Hazir
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Correspondence to Corinne Autant-Bernard .

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Editors and Affiliations

  1. Innovation Systems Department, AIT Austrian Institute of Technology, Wien, Austria

    Thomas Scherngell

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Autant-Bernard, C., Hazir, Ç.S. (2013). Network Formation and Geography: Modelling Approaches, Underlying Conceptions, Recent and Promising Extensions. In: Scherngell, T. (eds) The Geography of Networks and R&D Collaborations. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-02699-2_2

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