The human connectome is constructed by translating neuron activities into a complex network with nodes and edges. Although the topology of such networks is well studied, the formation rules and dynamics are not fully understood. It is challenging to develop a generative model for large scale dynamic complex networks due to the computational obstacle and nontrivial network structure. To study the node-based and subject-based network dynamics in resting-state brain, we divide the brain scan session into several sliding windows and generate a network for each segment. Then, an action-based model generator, which generates edges according to a topology manipulating actions, is fitted to the network series. This model, presumably as the first applicable approach for synthesizing the brain network dynamics, is shown capable of synthesizing a network series of the action-based model. Also, the estimated parameters shows that the actions of brain regions are related to their functionality.