DrunkardMob: billions of random walks on just a PC.

ABSTRACTRandom walks on graphs are a staple of many ranking and recommendation algorithms. Simulating random walks on a graph which fits in memory is trivial, but massive graphs pose a problem: the latency of following walks across network in a cluster or loading nodes from disk on-demand renders basic random walk simulation unbearably inefficient. In this work we propose DrunkardMob, a new algorithm for simulating hundreds of millions, or even billions, of random walks on massive graphs, on just a single PC or laptop. Instead of simulating one walk a time it processes millions of them in parallel, in a batch. Based on DrunkardMob and GraphChi we further propose a framework for easily expressing scalable algorithms based on graph walks.