Fast Algorithms for Top-k Personalized PageRank Queries

摘要：

In entity-relation (ER) graphs (V,E), nodes V represent typed entities and edges E represent typed relations. For dynamic personalized PageRank queries, nodes are ranked by their steady-state probabilities obtained using the standard random surfer model. In this work, we propose a framework to answer top-k graph conductance queries. Our top-k ranking technique leads to a 4×speedup, and overall, our system executes queries 200-600×faster than whole-graph PageRank. Some queries might contain hard predicates I.e. Predicates that must be satis.ed by the answer nodes. E.g., we may seek authoritative papers on public key cryptography, but only those written during 1997. We extend our system to handle hard predicates. Our system achieves these substantial query speedups while consuming only 10-20％ of the space taken by a regular text index.

关键词： top-k Pagerank HubRank Node-deletion personalized

作者: Manish Gupta Amit Pathak Soumen Chakrabarti

作者单位: IIT Bombay

会议类型: 国际会议

会议名称: 第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-04-21（万方平台首次上网日期，不代表论文的发表时间）