Unifying Inference of Meso-Scale Structures in Networks.
Unifying Inference of Meso-Scale Structures in Networks.
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DOI：
10.1371/journal.pone.0143133
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Verma R
中科院分区：
文献类型：
--
作者：
Tunç B;Verma R
网络是科学研究中最流行的形式表示之一，用于描述分子，神经元簇或社会群体等对象之间的相互作用。在中观尺度上进行的研究涉及基于其独特的交互模式对对象进行分组，这是网络科学研究的主线之一。例如，在社交网络中，中尺度结构可以对应于作为通信核心的孤立的社会群体或个人群体。目前，对不同的中尺度结构，如社区和核心-外围结构的研究已经通过独立的方法进行，这排除了可以处理多个中尺度结构的算法设计的可能性，并决定哪种结构更好地解释了观测数据。在这项研究中，我们提出了一个统一的配方算法检测和分析不同的中尺度结构。这有利于混合结构的调查，捕捉多个中尺度结构和竞争结构的统计比较之间的相互作用，所有这些都是迄今为止不可用的。我们证明了该方法在分析人脑网络中的适用性，通过确定大脑的主导组织结构（社区），以及其辅助特征（核心-外围）。
Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).