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首页> 外文期刊>Proceedings of the IEEE >Overlapping Communities Explain Core–Periphery Organization of Networks
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Overlapping Communities Explain Core–Periphery Organization of Networks

机译:重叠的社区解释了网络的核心-外围组织

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摘要

Networks provide a powerful way to study complex systems of interacting objects. Detecting network communities—groups of objects that often correspond to functional modules—is crucial to understanding social, technological, and biological systems. Revealing communities allows for analysis of system properties that are invisible when considering only individual objects or the entire system, such as the identification of module boundaries and relationships or the classification of objects according to their functional roles. However, in networks where objects can simultaneously belong to multiple modules at once, the decomposition of a network into overlapping communities remains a challenge. Here we present a new paradigm for uncovering the modular structure of complex networks, based on a decomposition of a network into any combination of overlapping, nonoverlapping, and hierarchically organized communities. We demonstrate on a diverse set of networks coming from a wide range of domains that our approach leads to more accurate communities and improved identification of community boundaries. We also unify two fundamental organizing principles of complex networks: the modularity of communities and the commonly observed core–periphery structure. We show that dense network cores form as an intersection of many overlapping communities. We discover that communities in social, information, and food web networks have a single central dominant core while communities in protein–protein interaction (PPI) as well as product copurchasing networks have small overlaps and form many local cores.
机译:网络提供了一种强大的方式来研究复杂的交互对象系统。检测网络社区(通常与功能模块相对应的对象组)对于理解社会,技术和生物系统至关重要。揭示社区可以分析仅考虑单个对象或整个系统时不可见的系统属性,例如模块边界和关系的标识或根据对象的功能角色对对象进行分类。但是,在对象可以同时同时属于多个模块的网络中,将网络分解为重叠的社区仍然是一个挑战。在此,我们将网络分解为重叠,不重叠和分层组织的社区的任意组合,从而为揭示复杂网络的模块化结构提供了一种新的范例。我们在来自广泛领域的各种网络中证明,我们的方法可导致更准确的社区和对社区边界的更好识别。我们还统一了复杂网络的两个基本组织原则:社区的模块化和通常观察到的核心-外围结构。我们表明,密集的网络核心形成为许多重叠社区的交集。我们发现,社交,信息和食品网络网络中的社区只有一个中央主导核心,而蛋白质间相互作用(PPI)以及产品共同购买网络中的社区则有很小的重叠并形成许多本地核心。

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