Adaptive networks: theory, models and applications - download pdf or read online

By Thilo Gross, Hiroki Sayama

ISBN-10: 3642012841

ISBN-13: 9783642012846

With adaptive, complicated networks, the evolution of the community topology and the dynamical methods at the community are both very important and sometimes essentially entangled.

Recent learn has proven that such networks can show a plethora of latest phenomena that are eventually required to explain many real-world networks. a few of these phenomena comprise powerful self-organization in the direction of dynamical criticality, formation of complicated worldwide topologies in accordance with easy, neighborhood principles, and the spontaneous department of "labor" within which an before everything homogenous inhabitants of community nodes self-organizes into functionally unique periods. those are only a number of.

This ebook is a state of the art survey of these precise networks. In it, best researchers got down to outline the long run scope and course of a few of the main complicated advancements within the titanic box of advanced community technology and its applications.

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Example text

S and τ are positively correlated: larger communities are on average older (Fig. 13a), which is quite natural, as communities are usually born small, and it takes time to recruit new members to reach a large size. 1 140 0 5 10 15 20 t 25 30 35 40 Fig. 13 (a) The average age τ of communities with a given size (number of people) s, divided by the average age of all communities τ , as the function of s, indicating that larger communities are on average older. (b) The average auto-correlation function C(t) of communities with different sizes (the unit of time, t, is one month).

Obviously, q must be smaller than Nr even in the initial state). To be able to compare the results of the model with the community structure of the co-authorship network, the runs were stopped when the number of nodes in the model reached the size of the co-authorship network. Our experience showed that the model is quite insensitive to changes in r or q, and μ is the only important parameter. 3) the resulting community degree distribution is truncated, whereas when μ is too large (μ > 1), a giant community with abnormally large community degree appears.

Furthermore, a significant number of additional nodes can be also classified into the discovered communities. For example, if a node not yet classified has link(s) only to a single community (and, if it has no links connecting to nodes in any other community) it can be safely added to that community. 4 million users into communities. Another important statistics describing the community system is the community size distribution. In Fig. 12a we show the community size distribution in the phone-call network at different time steps.

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Adaptive networks: theory, models and applications by Thilo Gross, Hiroki Sayama


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