Identifying gene regulatory network (GRN) from time course gene expression data has attracted more and more attentions. Due to the computational complexity, most approaches for GRN econstruction are limited on a small number of genes and low connectivity of the underlying networks. These approaches can only identify a single network for a given set of genes. However, for a large-scale gene network, there might exist multiple potential sub-networks, in which genes are only functionally related to thers in the sub-networks. In this paper, we propose an efficient algorithm for identifying multiple sub-networks from gene expression data by incorporating community structure information into GRN inference. The proposed algorithm iteratively solves two optimization problems, and thus promisingly applies to large-scale GRNs. Experimental studies on synthetic datasets validate the effectiveness of the proposed algorithm in the inference of sub-networks.