In this paper, we study two tightly coupled topics in selecting paper reviewers from authors’ scientific collaboration network (SCN): network construction and community detection. Based on the fact that the authors of one journal can be selected as reviewers and the reviewers of one manuscript should come from different research communities, we firstly evaluate the collaboration among all authors according to their signatures and construct the normalized collaboration network. For the second key problem of detecting the communities of one scientific collaboration network, considering it is much sparse and has few connections with inter community for one vertex, we apply the method of orthogonal matching pursuit to calculate compressive collaboration information. We conduct several experiments on simulated and real journal author datasets. Although there is no standard to evaluate different kinds of scientific collaboration network, the community detection accuracy rate and the stability of all authors are used to evaluate the performance of the proposed method. We can see from the vertex linkage matrix that our designed scientific collaboration network has good character of vertex grouping. The extensive study of our detection method in simulated data shows that the proposed method has a great advantage in the detection rate and stability. The significant improvement is about 60% compared with the classic methods.