Distributed Community Detection Optimization Algorithm for Complex Networks

Abstract

To investigate coupled oscillator model and community detection algorithm, an improvement method about the phase synchronization oscillator model and an optimized method (community detection via model modification) was proposed. By using the Kuramoto oscillator model as a basis, after joining the negative coupling strength, the nodes can be divided into several different synchronized clusters. In the synchronization process, the internal nodes in the same matrix are connected closely. In this method Kuramoto coupled oscillator model are expanded. The network can realize synchronization or be partitioned into several clusters depending on its structure. If all the nodes of the network are densely connected as a whole entity, synchronization will appear. If the network consists of several groups within which the connections are dense and between which the connections are sparser, the network will be partitioned into several clusters by their phases. The networks are divided into several communities because of this clustering phenomenon. The experiments show that the method is very promising. The simulation results in a variety of community structure that the proposed algorithm is an accurate, efficient and practical method for detecting community structure in networks.

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