Find modules in signed networks based on modularity optimization
Abstract
The signed network depicts individual cooperative or hostile attitude in a system. It is very important to study the characteristics of complex networks and predict individual attitudes by analyzing the attitudes of individuals and their neighbors, which can divide individuals into different modules or communities. To detect the modules in signed networks, first, a modularity function for signed networks is utilized on the basis of the existing modularity function. Then, a new module detection algorithm for signed networks has also been put forward, which has high efficiency. Finally, the algorithm has been applied on both artificial and real networks. The results show that the number of modules given by our proposed algorithm is consistent with that of the number of actual modules.
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