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wiki:stakeh_as_network_analysis [2014/12/13 14:53] – dominikcswiki:stakeh_as_network_analysis [2014/12/13 14:54] (current) – dominikcs
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-==== Stakeholder network analysis (//Dominik Cremer-Schulte, [[wiki:irstea|Irstea Grenoble]]//) ====+==== Stakeholder network analysis====
  
 Data on project participation of the stakeholders has also been used to analyse the stakeholder network behind the AS projects. For this, we have used social network graphs. These graphs are visual tools that enable us to explore proximity, relationships and their strengths between stakeholders, here the Alpine Space project partners. They have their foundation in social network analysis (see Hanneman and Riddle (2005) for an introduction) which is based on mathematical tools and graph theory. By definition, a social network is simply a set of actors (nodes), that may have relationships (edges) with one another. In our case, the network nodes are all identified stake holding institutions in the 30 AS projects along the 2007-2013 programming period. The list of stakeholders was created using the excel sheet from the JTS. However, this list might be subject to bias as different practices exist in declaring project partners, especially for large institutions such as regions, provinces, universities or research centres (head institution, sub-units). Therefore, and contrary to the main stakeholder analysis, we derived a second dataset by aggregating stakeholders according to their head institutions. In the aggregated dataset, the number of stakeholders dropped from 231 to 189, i.e. 40 institutions are in fact sub-units of head institutions. This generalization gives us insights on the real importance of these head institutions; information that is not available in the disaggregated data. This consideration has consequences for the network graph. The edges are based on the collaborations with other stakeholders that took place during the projects. We do not consider variations in collaboration intensity (e.g. different intensities of collaboration in general, timely variation) as we did not have available such qualitative data. Although data might not appear rich on first sight (lack of intensity), the resulting graphs provide us with valuable insights on the (partial, only 28 projects) network established in the framework of the AS programme. Data on project participation of the stakeholders has also been used to analyse the stakeholder network behind the AS projects. For this, we have used social network graphs. These graphs are visual tools that enable us to explore proximity, relationships and their strengths between stakeholders, here the Alpine Space project partners. They have their foundation in social network analysis (see Hanneman and Riddle (2005) for an introduction) which is based on mathematical tools and graph theory. By definition, a social network is simply a set of actors (nodes), that may have relationships (edges) with one another. In our case, the network nodes are all identified stake holding institutions in the 30 AS projects along the 2007-2013 programming period. The list of stakeholders was created using the excel sheet from the JTS. However, this list might be subject to bias as different practices exist in declaring project partners, especially for large institutions such as regions, provinces, universities or research centres (head institution, sub-units). Therefore, and contrary to the main stakeholder analysis, we derived a second dataset by aggregating stakeholders according to their head institutions. In the aggregated dataset, the number of stakeholders dropped from 231 to 189, i.e. 40 institutions are in fact sub-units of head institutions. This generalization gives us insights on the real importance of these head institutions; information that is not available in the disaggregated data. This consideration has consequences for the network graph. The edges are based on the collaborations with other stakeholders that took place during the projects. We do not consider variations in collaboration intensity (e.g. different intensities of collaboration in general, timely variation) as we did not have available such qualitative data. Although data might not appear rich on first sight (lack of intensity), the resulting graphs provide us with valuable insights on the (partial, only 28 projects) network established in the framework of the AS programme.
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   * Levallois, C. (2014): [[http://www.clementlevallois.net/gephi/tuto/en/gephi_advanced%20functions_en.pdf|Gephi - Advanced functions. Filters, metrics, plugins and workspaces]]. Gephi Support Team, Lyon Business School.   * Levallois, C. (2014): [[http://www.clementlevallois.net/gephi/tuto/en/gephi_advanced%20functions_en.pdf|Gephi - Advanced functions. Filters, metrics, plugins and workspaces]]. Gephi Support Team, Lyon Business School.
   * Biemann, C. (2006): [[http://wortschatz.uni-leipzig.de/~cbiemann/pub/2006/BiemannTextGraph06.pdf|Chinese Whispers - an Efficient Graph Clustering Algorithm and its Application to Natural Language Processing Problems]]. Proceedings of the HLT-NAACL-06 Workshop on Textgraphs-06, New York, USA.   * Biemann, C. (2006): [[http://wortschatz.uni-leipzig.de/~cbiemann/pub/2006/BiemannTextGraph06.pdf|Chinese Whispers - an Efficient Graph Clustering Algorithm and its Application to Natural Language Processing Problems]]. Proceedings of the HLT-NAACL-06 Workshop on Textgraphs-06, New York, USA.
 +
 +//Dominik Cremer-Schulte, [[wiki:irstea|Irstea Grenoble]], December 2014// 
wiki/stakeh_as_network_analysis.1418478816.txt.gz · Last modified: 2014/12/13 14:53 by dominikcs