Weighted APPR with restart for community extraction from multilayer networks

Community extraction is widely used for various applications concerning network analysis, but its scope has mostly been confined to single-layer network. However, the real-world data are frequently represented as multilayer network, and furthermore, each layer frequently exhibits different properties. It is thus critical to be able to control to what degree each layer should be reflected in the overall network, in order to perform a reliable community extraction. In this paper, we propose a superimposition method called WAPPRS (weighted APPR with restart) that can monotonically increase the degree of reflection and handle a wide range of values for reflection. Our experiments demonstrate that our proposed model outperforms previous models when dealing with the properties desirable for reliable community extraction from multilayer networks.