Automatic expert assignment is a common problem encoun- tered in both industry and academia. For example, for conference program chairs and journal editors, in order to collect "good " judgments for a paper, it is necessary for them to assign the paper to the most appropriate reviewers. Choosing appropriate reviewers of course includes a number of considerations such as expertise and authority, but also diversity and avoiding con icts. In this paper, we explore the expert retrieval problem and implement an automatic paper-reviewer recommendation system that considers as- pects of expertise, authority, and diversity. In particular, a graph is first constructed on the possible reviewers and the query paper, incorporating expertise and authority in- formation. Then a Random Walk with Restart (RWR)  model is employed on the graph with a sparsity constraint, incorporating diversity information. Extensive experiments on two reviewer recommendation benchmark datasets show that the proposed method obtains performance gains over state-of-the-art reviewer recommendation systems in terms of expertise, authority, diversity, and, most importantly, rele- vance as judged by human experts.