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Evolution of Social Networks

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Research , Preprint , Review 01 Jan 2012 Germany, United Kingdom English Publisher:Elsevier BVJournal:SSRN Electronic Journal (eissn: 1556-5068,

Authors: Hellmann, Tim; Staudigl, Mathias;

doi: 10.2139/ssrn.2321900 , 10.1016/j.ejor.2013.08.022

handle: 10419/81086

Evolution of Social Networks

- Summary
- Subjects
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Abstract

Modeling the evolution of networks is central to our understanding of modern large communication systems, such as the World-Wide-Web, as well as economic and social networks. The research on social and economic networks is truly interdisciplinary and the number of modeling strategies and concepts is enormous. In this survey we present some modeling approaches, covering classical random graph models and game-theoretic models, which may be used to provide a unified framework to model and analyze the evolution of networks.

Countries

Germany, United Kingdom

Related Organizations

University of Southampton
United Kingdom
Bielefeld University
Germany
University of Mannheim
Germany

Keywords

Spieltheorie, 330, ddc:330, Soziales Netzwerk, Evolution of networks, 510, C73, D83, Graphentheorie, Stochastic processes, Evolutionsökonomik, D85, Theorie, Game theory, Random graphs

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	52
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%