Highlighted notes on:
I/O-Efficient Techniques for Computing Pagerank.
While doing research work with:
Prof. Dip Sankar Banerjee and Prof. Kishore Kothapalli:
https://dl.acm.org/doi/10.1145/584792.584882
This paper describes a technique to partition the link file of the whole file into blocks of a range of destination nodes, with partial source nodes, so that it is possible to run power iteration of pagerank of massive graphs which do not fit in memory. The graphs must be stored on disk, and partitions of the graphs are scanned in every iteration until the ranks converge. Unlike Haveliwala's technique, this is similar to pull based pagerank. Both methods have similarities with join techniques in database systems. Topic-sensitive pagerank is also discussed which finds pagerank of graphs related to a specific keywords beforehand, and merges them together based upon the query (might return better results than global pagerank). This requires small adjustments to the random jump probability factor (1-d).