{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/personalized-pagerank-estimation-and-search-a","title":"Personalized PageRank Estimation and Search: A Bidirectional Approach","arxiv_id":"1507.05999","date":"2015-07-21","proceeding":null,"authors":["Peter Lofgren","Siddhartha Banerjee","Ashish Goel"],"abstract":"We present new algorithms for Personalized PageRank estimation and\nPersonalized PageRank search. First, for the problem of estimating Personalized\nPageRank (PPR) from a source distribution to a target node, we present a new\nbidirectional estimator with simple yet strong guarantees on correctness and\nperformance, and 3x to 8x speedup over existing estimators in experiments on a\ndiverse set of networks. Moreover, it has a clean algebraic structure which\nenables it to be used as a primitive for the Personalized PageRank Search\nproblem: Given a network like Facebook, a query like \"people named John\", and a\nsearching user, return the top nodes in the network ranked by PPR from the\nperspective of the searching user. Previous solutions either score all nodes or\nscore candidate nodes one at a time, which is prohibitively slow for large\ncandidate sets. We develop a new algorithm based on our bidirectional PPR\nestimator which identifies the most relevant results by sampling candidates\nbased on their PPR; this is the first solution to PPR search that can find the\nbest results without iterating through the set of all candidate results.\nFinally, by combining PPR sampling with sequential PPR estimation and Monte\nCarlo, we develop practical algorithms for PPR search, and we show via\nexperiments that our algorithms are efficient on networks with billions of\nedges.","url_abs":"http://arxiv.org/abs/1507.05999v3","url_pdf":"http://arxiv.org/pdf/1507.05999v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"personalized-pagerank-estimation-and-search-a","repo_url":"https://github.com/yinyuan1227/strap-git","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1507.05999","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}