In this paper, we introduce a generative model named Author-Topic-Community (ATC) model which can infer authors' interests and their community structure at the same time based on the contents and citation information of a document corpus. Via the mutual promotion between the author topics and the author community structure introduced in the ATC model, the robustness of the model towards cases with spare citation information can be enhanced. Variational inference is adopted to estimate the model parameters of ATC. We performed evaluation using both synthetic data as well as a real dataset which contains SIGKDD and SIGMOD papers published in 10 years. By constrasting the performance of ATC with some state-of-the-art methods which model authors' interests and their community structure separately, our experimental results show that 1) the ATC model with the inference of the authors' interests and the community structure integrated can improve the accuracy of author topic modeling and that of author community discovery; and 2) more in-depth analysis of the authors' influence can be readily supported.