Brusco Michael, Stolze Hannah J, Hoffman Michaela, Steinley Douglas
Department of Marketing, Florida State University, Tallahassee, Florida, United States of America.
Business and Economics Department, Wheaton College, Wheaton, Illinois, United States of America.
PLoS One. 2017 May 9;12(5):e0170448. doi: 10.1371/journal.pone.0170448. eCollection 2017.
A popular objective criterion for partitioning a set of actors into core and periphery subsets is the maximization of the correlation between an ideal and observed structure associated with intra-core and intra-periphery ties. The resulting optimization problem has commonly been tackled using heuristic procedures such as relocation algorithms, genetic algorithms, and simulated annealing. In this paper, we present a computationally efficient simulated annealing algorithm for maximum correlation core/periphery partitioning of binary networks. The algorithm is evaluated using simulated networks consisting of up to 2000 actors and spanning a variety of densities for the intra-core, intra-periphery, and inter-core-periphery components of the network. Core/periphery analyses of problem solving, trust, and information sharing networks for the frontline employees and managers of a consumer packaged goods manufacturer are provided to illustrate the use of the model.
一种将一组参与者划分为核心子集和边缘子集的常用客观标准是，使与核心内部和边缘内部联系相关的理想结构与观察到的结构之间的相关性最大化。由此产生的优化问题通常使用启发式程序来解决，如重新定位算法、遗传算法和模拟退火算法。在本文中，我们提出了一种计算效率高的模拟退火算法，用于二元网络的最大相关性核心/边缘划分。该算法使用由多达2000个参与者组成的模拟网络进行评估，这些网络涵盖了网络核心内部、边缘内部以及核心与边缘之间部分的各种密度。文中还提供了对一家消费品制造商的一线员工和经理的问题解决、信任和信息共享网络的核心/边缘分析，以说明该模型的应用。
