A Dynamic Network Model - Simulation and Mean Field Analysis
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Von Händler/Antiquariat, BuchWeltWeit Inh. Ludwig Meier e.K. [57449362], Bergisch Gladbach, Germany.
Neuware - The aim of this work is to develop a dynamic model of stochastic network formation, where the nodes can connect both at random and through a preferential attachment of 'friends of friends.' In order to obtain mean field approximations we study the behaviour of the average degree and the clustering coefficient in a stationary state; we make use of the generating function method, among others. Through simulations, we demonstrate that depending on the calibration of parameters we obtain three different network structures: empty network, a giant component with few isolated nodes and a sparse network formed by different components. We mainly focus our analysis on the giant component which presents at least some of the key empirical regularities shared by socially generated networks. At the end of this work, we discuss the meaning of introduction of utility functions and their implications on stability and efficiency. 68 pp. Englisch.

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A Dynamic Network Model: The aim of this work is to develop a dynamic model of stochastic network formation, where the nodes can connect both at random and through a preferential attachment of `friends of friends.` In order to obtain mean field approximations we study the behaviour of the average degree and the clustering coefficient in a stationary state we make use of the generating function method, among others. Through simulations, we demonstrate that depending on the calibration of parameters we obtain three different network structures: empty network, a giant component with few isolated nodes and a sparse network formed by different components. We mainly focus our analysis on the giant component which presents at least some of the key empirical regularities shared by socially generated networks. At the end of this work, we discuss the meaning of introduction of utility functions and their implications on stability and efficiency. Englisch, Taschenbuch.

Lieferung aus: Deutschland, Versandkostenfrei innerhalb von Deutschland.
The aim of this work is to develop a dynamic model of stochastic network formation, where the nodes can connect both at random and through a preferential attachment of ´´friends of friends.´´ In order to obtain mean field approximations we study the behaviour of the average degree and the clustering coefficient in a stationary state; we make use of the generating function method, among others. Through simulations, we demonstrate that depending on the calibration of parameters we obtain three The aim of this work is to develop a dynamic model of stochastic network formation, where the nodes can connect both at random and through a preferential attachment of ´´friends of friends.´´ In order to obtain mean field approximations we study the behaviour of the average degree and the clustering coefficient in a stationary state; we make use of the generating function method, among others. Through simulations, we demonstrate that depending on the calibration of parameters we obtain three different network structures: empty network, a giant component with few isolated nodes and a sparse network formed by different components. We mainly focus our analysis on the giant component which presents at least some of the key empirical regularities shared by socially generated networks. At the end of this work, we discuss the meaning of introduction of utility functions and their implications on stability and efficiency. Sofort lieferbar Lieferzeit 1-2 Werktage.

Lieferung aus: Deutschland, Versandkosten nach: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, Syndikat Buchdienst, [4235284].
The aim of this work is to develop a dynamic model of stochastic network formation, where the nodes can connect both at random and through a preferential attachment of "friends of friends." In order to obtain mean field approximations we study the behaviour of the average degree and the clustering coefficient in a stationary state we make use of the generating function method, among others. Through simulations, we demonstrate that depending on the calibration of parameters we obtain three different network structures: empty network, a giant component with few isolated nodes and a sparse network formed by different components. We mainly focus our analysis on the giant component which presents at least some of the key empirical regularities shared by socially generated networks. At the end of this work, we discuss the meaning of introduction of utility functions and their implications on stability and efficiency. 2018, Taschenbuch / Paperback, Neuware, H: 220mm, B: 150mm, 68, Internationaler Versand, Selbstabholung und Barzahlung, PayPal, offene Rechnung, Banküberweisung.

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Von Händler/Antiquariat, expressbuch24.
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Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, European-Media-Service Mannheim [1048135], Mannheim, Germany.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen