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Consistency of community structure in complex networks
Physical Review E ( IF 2.4 ) Pub Date : 
Maria A. Riolo and M. E. J. Newman

The most widely used techniques for community detection in networks, including methods based on modularity, statistical inference, and information theoretic arguments, all work by optimizing objective functions that measure the quality of network partitions. There is a good case to be made, however, that one should not look solely at the single optimal community structure under such an objective function, but rather at a selection of high-scoring structures. If one does this one typically finds that the resulting structures show considerable variation, which could be taken as evidence that these community detection methods are unreliable, since they do not appear to give consistent answers. Here we argue that, upon closer inspection, the structures found are in fact consistent in a certain way. Specifically, we show that they can all be assembled from a set of underlying "building blocks," groups of network nodes that are usually found together in the same community. Different community structures correspond to different arrangements of blocks, but the blocks themselves are largely invariant. We propose an information theoretic method for discovering the building blocks in specific networks and demonstrate it with several example applications. We conclude that traditional community detection does in fact give a significant amount of insight into network structure.

中文翻译:

复杂网络中社区结构的一致性

网络中用于社区检测的最广泛使用的技术(包括基于模块化,统计推断和信息理论论证的方法)均通过优化用于测量网络分区质量的目标函数来起作用。但是,有一个很好的例子,一个人不应该只关注这种目标函数下的单个最佳社区结构,而应该关注高分结构的选择。如果这样做的话,通常会发现所得的结构显示出相当大的变异,这可以作为这些社区检测方法不可靠的证据,因为它们似乎无法给出一致的答案。在这里,我们认为,经过仔细检查,发现的结构实际上在某种程度上是一致的。特别,我们证明了它们都可以由一组底层的“构建块”组装而成,这些构建块通常是在同一社区中一起找到的网络节点组。不同的社区结构对应于不同的街区安排,但是街区本身在很大程度上是不变的。我们提出了一种信息理论方法,用于发现特定网络中的构建块,并通过几个示例应用程序对其进行演示。我们得出的结论是,传统的社区检测实际上确实提供了对网络结构的大量了解。我们提出了一种信息理论方法,用于发现特定网络中的构建块,并通过几个示例应用程序对其进行演示。我们得出的结论是,传统的社区检测实际上确实提供了对网络结构的大量了解。我们提出了一种信息理论方法,用于发现特定网络中的构建块,并通过几个示例应用程序对其进行演示。我们得出的结论是,传统的社区检测实际上确实提供了对网络结构的大量了解。
更新日期:2020-03-28
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