标题： 一种用于快速识别核心-外围结构的标签切换算法 显示英文标题

标题： A label-switching algorithm for fast core-periphery identification

摘要： 核心-外围（CP）结构经常出现在节点形成两个不同组的网络中：一个小而密集连接的核心和一个稀疏的外围。Borgatti 和 Everett（2000）提出了一种最流行的方法来识别和量化 CP 结构，通过将观察到的网络与一个“理想”的 CP 结构进行比较。尽管这个度量方法被广泛使用，但仍然需要一种改进的算法。在这项工作中，我们详细描述了一个贪婪的、标签切换的算法来识别既快速又准确的 CP 结构。通过利用 CP 度量的数学重述，我们提出的启发式方法相比简单实现，在操作次数上提供了数量级的改进。我们证明了该算法收敛到局部最小值，并且在小的玩具网络上始终产生接近全局最优解 90% 的解决方案。在合成网络上，我们的算法在分类准确率和运行时间上都优于一种流行的竞争方法；对真实网络的分析表明，所提出的方法可以比竞争对手快近 400 倍。 显示英文摘要

摘要： Core-periphery (CP) structure is frequently observed in networks where the nodes form two distinct groups: a small, densely interconnected core and a sparse periphery. Borgatti and Everett (2000) proposed one of the most popular methods to identify and quantify CP structure by comparing the observed network with an ``ideal'' CP structure. While this metric has been widely used, an improved algorithm is still needed. In this work, we detail a greedy, label-switching algorithm to identify CP structure that is both fast and accurate. By leveraging a mathematical reformulation of the CP metric, our proposed heuristic offers an order-of-magnitude improvement on the number of operations compared to a naive implementation. We prove that the algorithm converges to a local minimum while consistently yielding solutions within 90\% of the global optimum on small toy networks. On synthetic networks, our algorithm exhibits superior classification accuracies and run-times compared to a popular competing method, and the analysis of real-world networks shows that the proposed method can be nearly 400 times faster than the competition.