fg.pagerank: PageRank
In flashxio/FlashGraphR: Massive Graph Analysis

Description Usage Arguments Details Value Author(s) References

Compute the Google PageRank for a graph.

1	fg.page.rank(graph, no.iters = 1000, damping = 0.85)

`graph`	The FlashGraph object
`no.iters`	The number of iterations
`damping`	The damping factor ('d' in the original p)

This implementation computes PageRank values in the original PageRank paper below and does not normalize PageRank values in each iteration.

Sergey Brin and Larry Page: The Anatomy of a Large-Scale Hypertextual Web Search Engine. Proceedings of the 7th World-Wide Web Conference, Brisbane, Australia, April 1998.

To improve performance, a vertex only sends the difference of its PageRank value between the previous iteration and the current iteration to its neighbors in each iteration. If the difference is smaller than a threshold, a vertex does not send the difference to its neighbors. The algorithm converges if all vertices stop sending messages.

A numeric vector that contains PageRank values of each vertex.

Da Zheng <dzheng5@jhu.edu>

Sergey Brin and Larry Page: The Anatomy of a Large-Scale Hypertextual Web Search Engine. Proceedings of the 7th World-Wide Web Conference, Brisbane, Australia, April 1998.

flashxio/FlashGraphR documentation built on May 8, 2019, 11:11 p.m.