Efficient algorithms for approximate single-source personalized pagerank queries

Sibo Wang, Renchi Yang, Runhui Wang, Xiaokui Xiao, Zhewei Wei*, Wenqing Lin, Yin Yang, Nan Tang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

30 Citations (Scopus)


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. An important variant of the PPR query is single-source PPR (SSPPR), which enumerates all nodes in G and returns the top-k nodes with the highest PPR values with respect to a given source s. PPR in general and SSPPR in particular have important applications in web search and social networks, e.g., in Twitter’s Who-To-Follow recommendation service. However, PPR computation is known to be expensive on large graphs and resistant to indexing. Consequently, previous 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 effective index-free and index-based algorithms for approximate PPR processing, with rigorous guarantees on result quality. We first present FORA, an approximate SSPPR solution that combines 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 both high accuracy and efficiency. 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 the proposed solutions are orders of magnitude more efficient than their respective competitors. Notably, on a billion-edge Twitter dataset, FORA answers a top-500 approximate SSPPR query within 1s, using a single commodity server.
Original languageEnglish
Article number18
Number of pages37
JournalACM Transactions on Database Systems
Issue number4
Publication statusPublished - Dec 2019

Scopus Subject Areas

  • Information Systems

User-Defined Keywords

  • Forward push
  • Personalized PageRank
  • Random walk


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