Dynamics On and Of Complex Networks: Applications to Biology, Computer Science, and the Social Sciences (Modeling and Simulation in Science, Engineering and Technology)

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ISBN 10: 0817647503 / ISBN 13: 9780817647506

Edité par Birkhäuser, 2009

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Détails bibliographiques

• Titre: Dynamics On and Of Complex Networks: Applications to Biology, Computer Science, and the Social Sciences (Modeling and Simulation in Science, Engineering and Technology)
• Éditeur: Birkhäuser
• Date de parution: 2009
• Langue: anglais
• ISBN à 10 chiffres: 0817647503
• ISBN à 13 chiffres: 9780817647506
• Reliure: Hardcover
• État de l'article: Very Good

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Synopsis

In the context of network theory, Complex networks can be de?ned as a collection of nodes connected by edges representing various complex int- actions among the nodes. Almost any large-scale system, be it natural or man-made, can be viewed as a complex network of interacting entities, which is dynamically evolving over time. Naturally occurring networks include - ological, ecological and social networks (e. g., metabolic networks, gene r- ulatory networks, protein interaction networks, signaling networks, epidemic networks, food webs, scienti?c collaboration networks and acquaintance n- works), whereas man-made networks include communication networks and transportation infrastructures (e. g., the Internet, the World Wide Web, pe- to-peer networks, power grids and airline networks). This edited volume is a sequel to the workshop Dynamics on and of C- plex Networks (http: //www. cel. iitkgp. ernet. in/?eccs07/) held as a satellite event of the fourth European Conference on Complex Systems in Dresden, Germany from October 1-5, 2007. The primary aim of this workshop was to systematically explore the statistical dynamics "on" and "of" complex n- works that prevail across a large number of scienti?c disciplines. Dynamics on networks refers to the di?erent types of processes, for instance, prolife- tion and di?usion, that take place on networks. The functionality/e?ciency of these processes is strongly tied to the underlying topology as well as the dynamic behavior of the network.