Efficient and Decentralized PageRank Approximation

I read this very well-written paper. The authors set out to design a way to compute (an approximated) pagerank in a distributed and efficient way.

"Starting with the local graph G of a peer, the peer first extends G by adding a special node W, called world node since its role is to represent all pages in the network that do not belong to G. An initial JXP score for local pages and the world node is obtained by running the PR algorithm in the extended local graph G' = G+W.
...
we take all the links from local pages to external pages and make them point to the world node. ... as the peer learns about external links that point to one of the local pages, we assign these links to the world node."


Optimized Merging

"At a peer meeting, instead of merging the graphs and world nodes, we could simply add relevant information received from the other peer into the local world node, and perform the PR computation on the extended local graph and still the JXP scores converge to the global PR scores."