2017 年 12 巻 p. 352-362
Many models synthesize various types of complex networks with communities. However, a network generation model that can represent high-modularity networks is rare. In this paper, we propose a high-modularity network generation model by layer aggregation based on a multilayer network. Because people belong to many communities in society, such as family, school, hobby group, and business organizations, each example is regarded as a community in a single layer of a multilayer network. However, measuring each relationship in each community is difficult. A network on social network services (SNSs) that can be observed combines all communities. That is, a social network is generated from a multilayer network. A synthesized network in our model has either a community structure or a high-modularity structure. We apply the proposed model to generate a number of networks and compare them with real-world networks. Not only did it successfully represent real-world networks but we also found that we can predict how real-world networks are generated from the model's parameters.