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      State power and elite autonomy: The board interlock network of Chinese non-profits

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          Abstract

          In response to failures of central planning, the Chinese government has experimented not only with free-market trade zones, but with allowing non-profit foundations to operate in a decentralized fashion. A network study shows how these foundations have connected together by sharing board members, in a structural parallel to what is seen in corporations in the United States. This board interlock leads to the emergence of an elite group with privileged network positions. While the presence of government officials on non-profit boards is widespread, state officials are much less common in a subgroup of foundations that control just over half of all revenue in the network. This subgroup, associated with business elites, not only enjoys higher levels of within-elite links, but even preferentially excludes government officials from the nodes with higher degree. The emergence of this structurally autonomous sphere is associated with major political and social events in the state-society relationship.

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          Most cited references12

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          Power-law distributions in empirical data

          Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.
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            The Paradox of Civil Society

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              The Rich-Club Phenomenon In The Internet Topology

              , (2003)
              We show that the Internet topology at the Autonomous System (AS) level has a rich--club phenomenon. The rich nodes, which are a small number of nodes with large numbers of links, are very well connected to each other. The rich--club is a core tier that we measured using the rich--club connectivity and the node--node link distribution. We obtained this core tier without any heuristic assumption between the ASes. The rich--club phenomenon is a simple qualitative way to differentiate between power law topologies and provides a criterion for new network models. To show this, we compared the measured rich--club of the AS graph with networks obtained using the Barab\'asi--Albert (BA) scale--free network model, the Fitness BA model and the Inet--3.0 model.
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                Author and article information

                Journal
                2016-06-26
                Article
                1606.08103
                bed82be7-3767-400a-b6e9-938a740fc0b3

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                30 pages, 5 figures. Comments welcome
                cs.SI nlin.AO physics.soc-ph

                Social & Information networks,General physics,Nonlinear & Complex systems
                Social & Information networks, General physics, Nonlinear & Complex systems

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