Node-Centric Random Walk for Fast Index-Free Personalized PageRank

Abstract

Personalized PageRank (PPR) is a popular graph computation in various real-world applications. Since massive real-world graphs are evolving rapidly, PPR computation methods require index-free and fast. In general, index-free methods go through Forward Push phase and random walk Monte-Carlo simulation phase respectively. While existing methods have succeeded in accelerating the Forward Push phase, there is a space for running-time improvements in the second phase that performs a large number of sequential random walks. Through this sequential process, each random walk needs to obtain neighbor nodes for every single step, which causes redundant operation at each node as a result. Our proposal is a node-centric random walk that aggregates random walks at each node and minimizes the total number of obtaining neighbor nodes in the second phase. Most of the random walks can be aggregated while maintaining theoretical guarantees because they do not need to memorize the starting node. In addition, we review the expected running time of random walk Monte-Carlo simulation focusing on the total number of obtaining neighbor nodes. We conducted extensive experiments using four real-world graphs. Experimental results showed that our proposed method is up to 3.3x faster than the existing methods.