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arXiv 1608.00623stat.MEcs.SIphysics.soc-ph
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多层网络中的网络模型与社区发现

Null Models and Community Detection in Multi-Layer Networks

Subhadeep Paul, Yuguo Chen

摘要:

多层网络是一组实体(节点)上的网络,其中具有多种类型的关系(边),其中每种类型的关系/交互被表示为网络层。与单层网络一样,社区检测是多层网络中的一项重要任务。网络中的一大组流行社区检测方法是基于优化称为模块性分数的质量函数,这是网络中模块或社区存在的度量。因此,社区检测的第一步是定义一个适合于所讨论网络的适当模块化分数。在这里,我们介绍了几个多层网络模块化措施在不同的网络空模型,从不同的应用领域的网络经验观察的动机。特别是,我们定义的多层配置模型,多层预期度模型和它们的各种修改为空模型的多层网络,以获得不同的模块。拟议的模块分为两类。第一类是基于度校正的多层随机块模型,以多层期望度模型作为其零模型。第二类是基于Newman-Girvan模块化的多层扩展,将多层配置模型作为其零模型。这些措施,然后优化,以检测最佳社区分配的节点。我们在模拟网络中比较了社区发现措施的有效性,然后将其应用于四个真实网络。

英文摘要:

Multi-layer networks are networks on a set of entities (nodes) with multiple types of relations (edges) among them where each type of relation/interaction is represented as a network layer. As with single layer networks, community detection is an important task in multi-layer networks. A large group of popular community detection methods in networks are based on optimizing a quality function known as the modularity score, which is a measure of presence of modules or communities in networks. Hence a first step in community detection is defining a suitable modularity score that is appropriate for the network in question. Here we introduce several multi-layer network modularity measures under different null models of the network, motivated by empirical observations in networks from a diverse field of applications. In particular we define the multi-layer configuration model, the multi-layer expected degree model and their various modifications as null models for multi-layer networks to derive different modularities. The proposed modularities are grouped into two categories. The first category, which is based on degree corrected multi-layer stochastic block model, has the multi-layer expected degree model as their null model. The second category, which is based on multi-layer extensions of Newman-Girvan modularity, has the multi-layer configuration model as their null model. These measures are then optimized to detect the optimal community assignment of nodes. We compare the effectiveness of the measures in community detection in simulated networks and then apply them to four real networks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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