Ключевой игрок

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Ключевой игрок (ключевая фигура) - игрок, через которого строятся взаимодействия других игроков. Это игрок, который обладает наибольшей центральностью по посредничеству.

См. Анализ_социальных_сетей/Ключевые_понятия

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Key players are those elements in the network that are considered important, in regard to some criteria. One of mostly popular criteria the centrality of vertex. In graph theory and network analysis, there are various measures of the centrality of a vertex within a graph that determine the relative importance of a vertex within the graph. Degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality are the four measures of centrality that are widely used in network analysis. Freeman defined the betweenness measure as sums the proportion of shortest paths from one node to another that pass through a given node. A node with high betweenness centrality is responsible for connecting many pairs of nodes via the best path, and deleting that node should cause many pairs of nodes to be more distinctly. Deleting that node should cause many pairs of nodes to become fully disconnected or at least more distantly connected.

Литература

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  3. Campbell R. Object-relational Databases: The Key Players // Data Based Advis. 1997. Т. 15. № 9. С. 28–31.
  4. Goede B. de и др. Succinct Summaries of Narrative Events Using Social Networks // Proceedings of the 22Nd ACM Conference on Hypertext and Hypermedia HT ’11. New York, NY, USA: ACM, 2011. С. 299–304.
  5. Everett M.G., Borgatti S.P. The centrality of groups and classes // The Journal of Mathematical Sociology. 1999. Т. 23. № 3. С. 181–201.
  6. Klischewski R., Scholl H.J. (Jochen). Information Quality As a Common Ground for Key Players in e-Government Integration and Interoperability // Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 04 HICSS ’06. Washington, DC, USA: IEEE Computer Society, 2006. С. 72–.
  7. Lindelauf R., Blankers I. Key Player Identification: A Note on Weighted Connectivity Games and the Shapley Value // Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining ASONAM ’10. Washington, DC, USA: IEEE Computer Society, 2010. С. 356–359.
  8. Ortiz-Arroyo D., Hussain D.M. An Information Theory Approach to Identify Sets of Key Players // Proceedings of the 1st European Conference on Intelligence and Security Informatics EuroISI ’08. Berlin, Heidelberg: Springer-Verlag, 2008. С. 15–26.
  9. Park A.J., Tsang H.H. Detecting Key Players in Criminal Networks Using Dynalink // Proceedings of the 2013 European Intelligence and Security Informatics Conference EISIC ’13. Washington, DC, USA: IEEE Computer Society, 2013. С. 208–211.
  10. Puade O.A., Wyeld T.G. Visualising Collaboration via Email: Finding the Key Players // Proceedings of the Conference on Information Visualization IV ’06. Washington, DC, USA: IEEE Computer Society, 2006. С. 124–129.
  11. Sarangi S., Unlu E. Key Players and Key Groups in Teams: A Network Approach Using Soccer Data. Rochester, NY: Social Science Research Network, 2010.
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