We propose a growing network model and its learning algorithm. Unlike the conventional scale-free models, we incorporate community structure, which is an important characteristic of many real-world networks including the Web. In our experiments, we confirmed that the proposed model exhibits a degree distribution with a power-law tail, and our method can precisely estimate the probability of a new link creation from data without community information. Moreover, by introducing a measure of dynamic hub-degrees, we could predict the change of hub-degrees between communities.