Structurally cohesive subgroups are a powerful and mathematically rigorous way to characterize network robustness. Their strength lies in the ability to detect strong connections among vertices that not only have no neighbors in common, but that may be distantly separated in the graph. Unfortunately, identifying cohesive subgroups is a computationally intensive problem, which has limited empirical assessments of cohesion to relatively small graphs of at most a few thousand vertices. We describe here an approach that exploits the properties of cliques, k-cores and vertex separators to iteratively reduce the complexity of the graph to the point where standard algorithms can be used to complete the analysis. As a proof of principle, we apply our method to the cohesion analysis of a 29,462-vertex biconnected component extracted from a 128,151-vertex co-authorship data set.

中文翻译：

---

快速确定大型网络中的结构内聚子组。

结构上具有凝聚力的子组是表征网络健壮性的强大且数学上严格的方法。它们的优势在于能够检测顶点之间的强连接，这些顶点不仅没有共同的邻居，而且在图中可能相距很远。不幸的是，识别内聚子组是一个计算量大的问题，其对内聚的经验评估仅限于相对较小的图（最多几千个顶点）。我们在这里描述一种利用团簇，k核和顶点分隔符的属性的方法，以迭代方式将图的复杂性降低到可以使用标准算法完成分析的程度。作为原理上的证明，我们将我们的方法应用于从128个提取的29,462个顶点双向连接组件的内聚分析中，