Community detection method based on dynamic synchronous model

The invention belongs to the field of network data mining field, and specifically relates to a community detection method based on a dynamic synchronous model. The method comprises the steps of firstly reading social network data, and performing network vectorization according to a social network graph to obtain a vectorized one-dimensional coordinate sequence; setting synchronization parameters and calculating a synchronization range; performing synchronization clustering, wherein each node is synchronized in the synchronization range according to the extensional synchronous model until a local synchronization status is available; dividing communities according to the coordinate position of each node; calculating the modularity of the division; adding the synchronization parameters constantly; executing a new round of synchronization clustering process until the synchronization range covers all the nodes. Nodes in the network are clustered through a kuramoto model, so that a link density can be accurately described, the difference of the network link density is effectively reflected, the automatic detection of a social network community structure is realized, and the community detection results are selected and optimized.