A label-switching algorithm for fast core-periphery identification

Eric Yanchenko; Srijan Sengupta

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arXiv · 2506.02069

A label-switching algorithm for fast core-periphery identification

Eric Yanchenko·Srijan Sengupta

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Abstract

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 monotonically ascends to a local maximum 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 on one-real world network, it is 340 times faster.

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Eric Yanchenko, Srijan Sengupta. 2026-01-26. A label-switching algorithm for fast core-periphery identification. https://arxiv.org/abs/2506.02069

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