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Constrained Community Detection in Multiplex Networks

机译：多重网络中的受限社区检测

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Constrained community detection is a kind of community detection taking given constraints into account to improve the accuracy of community detection. Optimizing constrained Hamiltonian is one of the methods for constrained community detection. Constrained Hamiltonian consists of Hamiltonian which is generalized modularity and constrained term which takes given constraints into account. Nakata proposed a method for constrained community detection in monoplex networks based on the optimization of constrained Hamiltonian by extended Louvain method. In this paper, we propose a new method for constrained community detection in multiplex networks. Multiplex networks axe the combinar tions of multiple individual networks. They can represent temporal networks or networks with several types of edges. While optimizing modularity proposed by Mucha et al. is popular for community detection in multiplex networks, our method optimizes the constrained Hamiltonian which we extend for multiplex networks. By using our proposed method, we successfully detect communities taking constraints into account. We also successfully improve the accuracy of community detection by using our method iteratively. Our method enables us to carry out constrained community detection interactively in multiplex networks.
机译：约束社区检测是一种将给定约束考虑在内以提高社区检测准确性的社区检测。优化约束哈密顿量是约束社区检测的方法之一。受约束的哈密顿量由哈密顿量组成，哈密顿量是广义的模块化和考虑了给定约束的约束项。 Nakata提出了一种基于扩展Louvain方法优化约束哈密顿量的单链网络中约束社区检测的方法。在本文中，我们提出了一种用于多路复用网络中受限社区检测的新方法。复用网络将多个单独的网络组合在一起。它们可以表示时间网络或具有几种类型的边的网络。在优化Mucha等人提出的模块性的同时。在多路复用网络中用于社区检测很流行，我们的方法优化了扩展到多路复用网络的约束哈密顿量。通过使用我们提出的方法，我们成功地将约束条件纳入考虑范围。通过迭代使用我们的方法，我们还成功地提高了社区检测的准确性。我们的方法使我们能够在Multiplex网络中以交互方式执行受限社区检测。

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