We develop a method to identify statistically significant communities in a weighted network with a high proportion of self-looping weights. We use this method to find overlapping agglomerations of U.S. counties by representing inter-county commuting as a weighted network. We identify three types of communities; non-nodal, nodal and monads, which correspond to different types of regions. The results suggest that traditional regional delineations that rely on ad hoc thresholds do not account for important and pervasive connections that extend far beyond expected metropolitan boundaries or megaregions.

中文翻译：

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在具有显着自循环的通勤网络中使用社区检测来划分地理区域

我们开发了一种方法来识别具有高比例自循环权重的加权网络中具有统计意义的社区。我们使用这种方法通过将县间通勤表示为加权网络来查找美国县的重叠聚集。我们确定了三种类型的社区；非节点、节点和单子，它们对应于不同类型的区域。结果表明，依赖于临时阈值的传统区域划分无法解释远远超出预期的大都市边界或超大区域的重要和普遍的联系。