PageRank is a popular ranking metric for large graphs such as the World Wide Web. Current research techniques for improving computational efficiency of PageRank have focussed on improving the I/O cost, convergence and parallelizing the computation process. In this paper, we propose a "divide and conquer" strategy for efficient computation of PageRank. The strategy is different from contemporary improvements in that it can be combined with any existing enhancements to PageRank, giving way to an entire class of more efficient algorithms. We present a novel graph-partitioning technique for dividing the graph into subgraphs, on which computation can be performed independently. This approach has two significant benefits. Firstly, since the approach focuses on work-reduction, it can be combined with any existing enhancements to PageRank. Secondly, the proposed approach leads naturally into developing an incremental approach for computation of such ranking metrics given that these large graphs evolve over a period of time. The partitioning technique is both lossless and independent of the type (variant) of PageRank computation algorithm used. The experimental results for a static single graph (graph at a single time instance) as well as for the incremental computation in case of evolving graphs, illustrate the utility of our novel partitioning approach. The proposed approach can also be applied for the computation of any other metric based on first order Markov chain model.