PageRank (PR) is the standard metric used by the Google search engine to compute the importance of a web page via modeling the entire web as a first order Markov chain. The challenge of computing PR efficiently and quickly has been already addressed by several works previously who have shown innovations in both algorithms and in the use of parallel computing. The standard method of computing PR is handled by modelling the web as a graph. The fast growing internet adds several new web pages everyday and hence more nodes (representing the web pages) and edges (the hyperlinks) are added to this graph in an incremental fashion. Computing PR on this evolving graph is now an emerging challenge since computations from scratch on the massive graph is time consuming and unscalable. In this work, we propose Hybrid Page Rank (HyPR), which computes PR on evolving graphs using collaborative executions on muti-core CPUs and massively parallel GPUs. We exploit data parallelism via efficiently partitioning the graph into different regions that are affected and unaffected by the new updates. The different partitions are then processed in an overlapped manner for PR updates. The novelty of our technique is in utilizing the hybrid platform to scale the solution to massive graphs. The technique also provides high performance through parallel processing of every batch of updates using a parallel algorithm. HyPR efficiently executes on a NVIDIA V100 GPU hosted on a 6th Gen Intel Xeon CPU and is able to update a graph with 640M edges with a single batch of 100,000 edges in 12 ms. HyPR outperforms other state of the art techniques for computing PR on evolving graphs  by 4.8x. Additionally HyPR provides 1.2x speedup over GPU only executions, and 95x speedup over CPU only parallel executions. © 2020 IEEE.