HubPPR: Effective Indexing for Approximate Personalized PageRank

Abstract

Personalized PageRank (PPR) computation is a fundamental operation in web search, social networks, and graph analysis. Given a graph G, a source s, and a target t, the PPR query Π(s, t) returns the probability that a random walk on G starting from s terminates at t. Unlike global PageRank which can be effectively pre-computed and materialized, the PPR result depends on both the source and the target, rendering results materialization infeasible for large graphs. Existing indexing techniques have rather limited effectiveness; in fact, the current state-of-the-art solution, BiPPR, answers individual PPR queries without pre-computation or indexing, and yet it outperforms all previous index-based solutions.

Motivated by this, we propose HubPPR, an effective indexing scheme for PPR computation with controllable tradeoffs for accuracy, query time, and memory consumption. The main idea is to pre-compute and index auxiliary information for selected hub nodes that are often involved in PPR processing. Going one step further, we extend HubPPR to answer top-k PPR queries, which returns the k nodes with the highest PPR values with respect to a source s, among a given set T of target nodes. Extensive experiments demonstrate that compared to the current best solution BiPPR, HubPPR achieves up to 10x and 220x speedup for PPR and top-k PPR processing, respectively, with moderate memory consumption. Notably, with a single commodity server, HubPPR answers a top-k PPR query in seconds on graphs with billions of edges, with high accuracy and strong result quality guarantees.