Toward Efficient Hub-Less Real Time Personalized PageRank

In the era of big data, reduced models capable of reducing big data graph to estimate personalized PageRank are limited. Personalized PageRank is a page rank calculation where random jumps are only allowed to a subset of start nodes. The resources of current process of calculation of personalized PageRank are highly prohibitive, thus in this paper we propose a novel fast accurate and less resource intensive algorithm to the personalized PageRank problem. FAST Personalized PageRank is utilized to find the target node set. Using the mentioned target set, the algorithm gives an estimation of the closeness of any pair of nodes in the graph. As the time taken by the estimation of personalized PageRank is directly proportional to the network size, in this paper a node reduction method is used to prune the graph. In this pruning model, most popular nodes also known as hubs are found using personalized page vector. To decrease the entropy and reduce the number of alternate paths to the target nodes, popular nodes are identified and flagged. The flagged nodes are, then, given a lower priority in the computation. This way the redundant path will being ignored in the computation process. After pruning the graph, estimation results achieve an improved time complexity. In our experiment, we compare our result with the benchmark FAST personalized PageRank approach. Our algorithm significantly reduces the computation time and outperforms the benchmark FAST personalized PageRank algorithm in highly dense graphs.