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Constrained Community Detection in Multiplex Networks

机译：多路复用网络中的受限群落检测

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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 are the combinations 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：受限的社区检测是一种社区检测，考虑到限制，以提高社区检测的准确性。优化约束Hamiltonian是约束社区检测的方法之一。约束的哈密顿人由汉密尔顿人组成，它是广义模块化和受约束的术语，这考虑了给定的约束。 Nakata提出了一种通过扩展Louvain方法的约束Hamiltonian的优化来限制Monoplex网络的群落检测方法。在本文中，我们提出了一种在多路复用网络中约束社区检测的新方法。多路复用网络是多个单独网络的组合。它们可以代表具有多种类型的边缘的时间网络或网络。在优化Mucha等人提出的模块化的同时。对于多路复用网络中的社区检测是流行的，我们的方法优化了我们为多路复用网络扩展的受限Hamiltonian。通过使用我们提出的方法，我们成功地检测社区考虑了约束。我们还通过使用我们的方法来成功提高社区检测的准确性。我们的方法使我们能够在多路复用网络中交互地进行约束的社区检测。

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