SubgraphRank: PageRank Approximation for a Subgraph or in a Decentralized System

PageRank, a ranking metric for hypertext web pages, has received increased interests. As the Web has grown in size, computing PageRank scores on the whole web using centralized approaches faces challenges in scalability. Distributed systems like peer-to-peer(P2P) networks are employed to speed up PageRank. In a P2P system, each peer crawls web fragments independently. Hence the web fragment on one peer is incomplete and may overlap with other peers. The challenge is to compute PageRank on each web fragment, reflecting the global web graph. Another interesting case is focused crawler, where only pages in a web fragment are of interest. In this research, we study the following problem: Given a web fragment and the whole web structure, approximate the global PageRank scores on subgraph, without running PageRank on the whole Web. We refine the PageRank paradigm to take into consideration the links connecting external pages. We describe a weight assigning approach to convey information about the global graph. We propose an efficient algorithm called SubgraphRank to compute the PageRank scores of a subgraph and design the experiments to validate the algorithm. In P2P case, we will relax the assumption of the global graph in future work.