Constrained Community Detection in Multislice Networks

Community detection is one of the methods for network analysis. It is useful for understanding, visualizing and compressing networks. A popular method for community detection is optimizing modularity which is the function for evaluating the result of community detection. Constrained community detection is a variation of community detection. It takes given constraints into account in order 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 based on the optimization of constrained Hamiltonian by extended Louvain method. He showed his method is sperior to previous method based on simulated annealing. In this paper, we propose a new method for constrained community detection in multislice networks. Multislice networks are the combinations of multiple individual networks, which have abilities of representing temporal networks and those with several types of edges. While optimizing Mucha’s modularity is popular for community detection in multislice networks, our method optimizes the constrained Hamiltonian which we extend for multislice 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 repeatedly. Our method enables us to carry out constrained community detection interactively in multislice networks.