A large variety of techniques has been developed for community structure identification (CSI) including modularity optimization, graph partitioning, and hierarchical clustering. In this paper, we argue that generative models are a promising approach for community structure identification, although these models have received very little attention from CSI researchers. Following the work of Cohn and Chang on link analysis, we propose a new probabilistic model for community structure detection. The originality of our model is the use of smoothing in order to overcome the sparsity of network data. A method based on the modularity criterion is also proposed for the estimation of smoothing parameters. Experiments carried out on three real datasets show that our new model SPCE (smoothed probabilistic community explorer) significantly outperforms PHITS (probabilistic HITS).