FORA: Simple and Effective Approximate Single-Source Personalized PageRank

Sibo Wang, Renchi Yang, Xiaokui Xiao, Zhewei Wei, Yi Yang

Abstract: Given a graph G, a source node s and a target node t, the personalized PageRank (PPR) of t with respect to s is the probability that a random walk starting from s terminates at t. A single-source PPR (SSPPR) query enumerates all nodes in G, and returns the top-k nodes with the highest PPR values with respect to a given source node s. SSPPR has important applications in web search and social networks, e.g., in Twitter's Who-To-Follow recommendation service. However, SSPPR computation is immensely expensive, and at the same time resistant to indexing and materialization. So far, existing solutions either use heuristics, which do not guarantee result quality, or rely on the strong computing power of modern data centers, which is costly. Motivated by this, we propose FORA, a simple and effective index-based solution for approximate SSPPR processing, with rigorous guarantees on result quality. The basic idea of FORA is to combine two existing methods Forward Push (which is fast but does not guarantee quality) and Monte Carlo Random Walk (accurate but slow) in a simple and yet non-trivial way, leading to an algorithm that is both fast and accurate. Further, FORA includes a simple and effective indexing scheme, as well as a module for top-k selection with high pruning power. Extensive experiments demonstrate that FORA is orders of magnitude more efficient than its main competitors. Notably, on a billion-edge Twitter dataset, FORA answers a top-500 approximate SSPPR query within 5 seconds, using a single commodity server.