Core-periphery structure is an important network feature where the network is broken into two components: a densely connected core and a loosely connected periphery. In this work, we propose a divide-and-conquer algorithm to identify the core-periphery structure in large networks. By finding this structure on much smaller sub-samples of the network and then combining the results across sub-samples, this method yields fast and accurate core-periphery labels. Additionally, the method provides a measure of the statistical significance of the structure. We apply our approach to synthetic data to find the algorithm's detection limit and on a real-world network with more than 35,000 nodes.

中文翻译：

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大型网络中核心-外围识别的分而治之算法

核心-外围结构是一个重要的网络特征，其中网络分为两个部分：密集连接的核心和松散连接的外围。在这项工作中，我们提出了一种分而治之的算法来识别大型网络中的核心-外围结构。通过在网络的小得多的子样本上找到这种结构，然后跨子样本组合结果，这种方法可以产生快速和准确的核心-外围标签。此外，该方法提供了结构统计显着性的度量。我们将我们的方法应用于合成数据，以找到算法的检测限，并在具有超过 35,000 个节点的真实网络上。