COMMUNITY DETECTION IN TEMPORAL MULTILAYER NETWORKS, WITH AN APPLICATION TO CORRELATION NETWORKS
COMMUNITY DETECTION IN TEMPORAL MULTILAYER NETWORKS, WITH AN APPLICATION TO CORRELATION NETWORKS
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DOI：
10.1137/15m1009615
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发表时间：
2016-01-01
影响因子：
1.6
通讯作者：
Howison, Sam D.
中科院分区：
文献类型：
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作者：
Bazzi, Marya;Porter, Mason A.;Howison, Sam D.
网络是表示交互实体复杂系统的便捷方法。许多网络都包含与网络其余部分中的节点相比，这些节点的“社区”更密集地连接。在本文中，我们研究了代表多层网络的时间网络中社区的检测。作为重点例子，我们研究了时间依赖于时间的财务资源相关网络。我们首先认为使用“模块化”质量功能的使用 - 通过将观察到的网络中的边缘权重与“ null Network” IS应用程序依赖性的预期边缘权重进行比较来定义。在讨论模块化最大化的讨论中，我们区分了“零网络”和“空模型”，我们强调说，相同的空网络可以对应于不同的空模型。然后，我们研究了多层模块化最大化问题，以识别时间网络中的社区。我们的多层分析仅取决于最大化问题的形式，而不取决于人们选择的特定质量功能。我们引入了诊断，以衡量多层网络分区中社区结构的持久性。我们证明了几种结果，这些结果描述了多层最大化问题如何衡量层中静态社区结构与跨层持久性较大的持续价值之间的权衡。我们还讨论了一些流行的“ Louvain”启发式面孔的计算问题，并通过时间多层网络提出了减轻它们的方法。
Networks are a convenient way to represent complex systems of interacting entities. Many networks contain "communities" of nodes that are more densely connected to each other than to nodes in the rest of the network. In this paper, we investigate the detection of communities in temporal networks represented as multilayer networks. As a focal example, we study time-dependent financial-asset correlation networks. We first argue that the use of the "modularity" quality function-which is defined by comparing edge weights in an observed network to expected edge weights in a "null network"-is application-dependent. We differentiate between "null networks" and "null models" in our discussion of modularity maximization, and we highlight that the same null network can correspond to different null models. We then investigate a multilayer modularity-maximization problem to identify communities in temporal networks. Our multilayer analysis depends only on the form of the maximization problem and not on the specific quality function that one chooses. We introduce a diagnostic to measure persistence of community structure in a multilayer network partition. We prove several results that describe how the multilayer maximization problem measures a trade-off between static community structure within layers and larger values of persistence across layers. We also discuss some computational issues that the popular "Louvain" heuristic faces with temporal multilayer networks and suggest ways to mitigate them.