Locality-sensitive and Re-use Promoting Personalized PageRank computations

Node distance/proximity measures are used for quantifying how nearby or otherwise related two or more nodes on a graph are. In particular, personalized PageRank (PPR) based measures of node proximity have been shown to be highly effective in many prediction and recommendation applications. Despite its effectiveness, however, the use of personalized PageRank for large graphs is difficult due to its high computation cost. In this paper, we propose a Locality-sensitive, Re-use promoting, approximate Personalized PageRank (LR-PPR) algorithm for efficiently computing the PPR values relying on the localities of the given seed nodes on the graph: (a) The LR-PPR algorithm is locality sensitive in the sense that it reduces the computational cost of the PPR computation process by focusing on the local neighborhoods of the seed nodes. (b) LR-PPR is re-use promoting in that instead of performing a monolithic computation for the given seed node set using the entire graph, LR-PPR divides the work into localities of the seeds and caches the intermediary results obtained during the computation. These cached results are then reused for future queries sharing seed nodes. Experiment results for different data sets and under different scenarios show that LR-PPR algorithm is highly-efficient and accurate.