Index design for dynamic personalized PageRank

Abstract

Personalized PageRank, related to random walks with restarts and conductance in resistive networks, is a frequent search paradigm for graph-structured databases. While efficient batch algorithms exist for static whole-graph PageRank, interactive query-time personalized PageRank has proved more challenging. Here we describe how to select and build indices for a popular class of PageRank algorithms, so as to provide real-time personalized PageRank and smoothly trade off between index size, preprocessing time, and query speed. We achieve this by developing a precise, yet efficiently estimated performance model for personalized PageRank query execution. We use this model in conjunction with a query workload in a cost-benefit type index optimizer. On millions of queries from CITESEER and its data graphs with 74-320 thousand nodes, our algorithm runs 50-400x faster than whole-graph PageRank, the gap growing with graph size. Index size is 10-20% of a text index. Ranking accuracy is above 94%.