Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization
This work addresses the problem of accurately modeling heterogeneous interactions in multiplex networks for researchers and practitioners in network analysis, though it is incremental as it builds on existing matrix factorization approaches.
The paper tackles community detection in multiplex networks by proposing a method that identifies both common and layer-specific communities, addressing the limitation of existing methods that only find common structures. The method, Multiplex Orthogonal Nonnegative Matrix Tri-Factorization, is evaluated on synthetic and real networks, showing competitive performance compared to state-of-the-art techniques.
Networks are commonly used to model complex systems. The different entities in the system are represented by nodes of the network and their interactions by edges. In most real life systems, the different entities may interact in different ways necessitating the use of multiplex networks where multiple links are used to model the interactions. One of the major tools for inferring network topology is community detection. Although there are numerous works on community detection in single-layer networks, existing community detection methods for multiplex networks mostly learn a common community structure across layers and do not take the heterogeneity across layers into account. In this paper, we introduce a new multiplex community detection method that identifies communities that are common across layers as well as those that are unique to each layer. The proposed method, Multiplex Orthogonal Nonnegative Matrix Tri-Factorization, represents the adjacency matrix of each layer as the sum of two low-rank matrix factorizations corresponding to the common and private communities, respectively. Unlike most of the existing methods, which require the number of communities to be pre-determined, the proposed method also introduces a two stage method to determine the number of common and private communities. The proposed algorithm is evaluated on synthetic and real multiplex networks, as well as for multiview clustering applications, and compared to state-of-the-art techniques.