Detecting community structure in complex networks based on K-means clustering and data field theory

Detecting community structure is fundamental for analyzing the relationship between structure and function in complex networks and for practical applications in many fields such as automatic control and economics. In this paper, after the introduction of the methods which is about the evaluation of the number of communities in the networks and the key node of each community, we propose two algorithms for network community structure detection: algorithm based on k-means clustering and algorithm based on data field theory. Finally, experiments show that the algorithms presented in this paper are of high accuracy with good performance and the “small-world” effect in the community is more obvious than in the whole network, which implies that it is more easier to reach synchronization in the community than in the whole network under the same coupling strength.