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Characterizing the relationship between degree distributions and community structures

机译:表征学位分布与社区结构之间的关系

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Extended power laws and inhomogeneous connections are structural patterns often found in empirical networks. Mechanisms based on the formation of triads are able to explain the power law behavior of the degree distribution of such networks. The proposed model introduces a two-step mechanism of attachment and triad formation that illustrates how preferential linkage plays an important role in shaping the inhomogeneity of connections and the division of the network into groups of nodes (i.e., the growth of community structures). In particular, we identify conditions under which the scaling exponent of the power law correlates to a widely-used modularity measure of non-overlapping communities. Our analytical results characterize the asymptotic behavior of both the scaling exponent and the modularity, as a function of the strength with which nodes with similar characteristics tend to link to each other.
机译:扩展的幂定律和不均匀的连接是经验网络中经常发现的结构模式。基于三合会形成的机制能够解释这种网络的度分布的幂律行为。提出的模型引入了一个由两步组成的依恋和三合会形成机制,该机制说明了优先链接在塑造连接的不均匀性以及将网络划分为节点组(即社区结构的增长)方面如何发挥重要作用。特别是,我们确定了幂律定标的指数与非重叠社区的广泛使用的模块化度量相关的条件。我们的分析结果将缩放指数和模块性的渐近行为特征化为强度,具有相似特征的节点趋于相互链接的强度。

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