A dynamical model for community detection in complex networks

One important feature observed in several complex networks is the structure of communities, or modular structure. Detecting communities is still a big challenge for researchers, specially the development of models to deal with dynamic networks. Here, we propose a new method for detecting communities by using a dynamical model. The first step consists of generating a spatial representation, named particle, for each vertex in the network. With these two representation, network structure and the spatial particles, we define the model's dynamics by means of two interactions types: the first is related to the network structure, or relational, and it is responsible for approaching particles representing neighbor vertices; the second, repulsive, is generated according to the spatial position of each particle and is responsible to make each unrelated particle, according to the network structure, to repel each other. Thus, after a couple of iteration, we observe the formation of groups of particles representing communities. On the other hand, distinct communities are separated according to the spatial positions of their particles. Simulation results show that our model achieves good results on the two benchmark models taken into account and that it can also deal with dynamic networks owing to its intrinsic dynamics.