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Community structure and dynamics in ...
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Stanford University.
Community structure and dynamics in social systems.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Community structure and dynamics in social systems.
作者:
Wilkinson, Dennis M.
面頁冊數:
176 p.
附註:
Adviser: Bernardo A. Huberman.
附註:
Source: Dissertation Abstracts International, Volume: 65-11, Section: B, page: 5776.
Contained By:
Dissertation Abstracts International65-11B.
標題:
Physics, General.
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3153057
ISBN:
9780496135035
Community structure and dynamics in social systems.
Wilkinson, Dennis M.
Community structure and dynamics in social systems.
- 176 p.
Adviser: Bernardo A. Huberman.
Thesis (Ph.D.)--Stanford University, 2005.
An automated method of identifying communities within a network of interactions is first presented. The method includes a statistical component crucial to obtaining accurate results in large, complex systems. It is applied to two real-world social networks, a network of email interactions and a network of related articles in the biomedical literature. The clusters it identifies within these networks are shown to correspond to communities of interrelated components.
ISBN: 9780496135035Subjects--Topical Terms:
227017
Physics, General.
Community structure and dynamics in social systems.
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Next, the dynamics of cooperative problem solving processes on social systems are studied. A simple stochastic model is proposed which captures key aspects of the dynamics which have been empirically observed. Most important among these are the increase in average time to solution and in likelihood of long delays as the system size increases, as well as the log-normal distribution of times to solution. It is shown that a community structure both reduces the average time to solution and decreases the probability of delay. In cases where a system of cooperative efforts does not possess an inherent community structure, the effect of imposing communities is examined. The factor which most affects the dynamics when communities are imposed is shown to be the degree to which individuals neglect information from outside their own communities.
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The theory of stochastic vector processes is central to the dynamics of social systems and a mathematical study of this subject is presented. Expressions describing the evolution of the moments in the neighborhood of fixed points are obtained for arbitrary systems. Approximation techniques are applied in the small and large noise limits, while an exact result is found for the second moment given a certain form of noise. The dependence of the moments on parameters such as system size and magnitude of noise is then examined. A critical value for the noise variance is found and used to determine a stability phase diagram for vector stochastic processes. Analytical results are validated by numerical simulation.
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This thesis presents applications of statistical physics to the study of the structure and dynamics of social systems, that is, systems whose interactions are based on information exchange. Social systems typically possess a community structure arising from the self organization of groups of interacting components into tightly-knit clusters.
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