PageRank is a classic measure that effectively evaluates the importance of nodes in large graphs. It has been applied in numerous applications spanning data mining, Web algorithms, recommendation systems, load balancing, search and connectivity structures identification. Computing PageRank for large graphs is challenging and this has motivated the studies of distributed algorithms to compute PageRank. Previously, little works have been spent on the distributed PageRank algorithms with strong guarantees on both complexity and accuracy. In this paper, we focus on the theoretical aspect and study the complexity of distributed PageRank computation based on the well-known congested-clique model with a bandwidth generalization. An existing algorithm proposed by Sarma et al. (2015) can be applied in this model, which estimates PageRanks in an n-node graph using, with high probability, O(logn) communication rounds and a bandwidth of O((logn)) bits. We present Radar-Push (RP), which is a distributed PageRank algorithm that is strictly better in round complexities. Specifically, Radar-Push uses O(loglogn) communication rounds and an edge bandwidth of O((logn)) bits. We further show that Radar-Push can be adapted to efficiently compute an important variant of PageRank, namely, the batch one-hop personalized PageRank, in O(loglogn) communication rounds.