Title
Efficient ad-hoc search for personalized PageRank
Abstract
Personalized PageRank (PPR) has been successfully applied to various applications. In real applications, it is important to set PPR parameters in an ad-hoc manner when finding similar nodes because of dynamically changing nature of graphs. Through interactive actions, interactive similarity search supports users to enhance the efficacy of applications. Unfortunately, if the graph is large, interactive similarity search is infeasible due to its high computation cost. Previous PPR approaches cannot effectively handle interactive similarity search since they need precomputation or approximate computation of similarities. The goal of this paper is to efficiently find the top-k nodes with exact node ranking so as to effectively support interactive similarity search based on PPR. Our solution is Castanet. The key Castanet operations are (1) estimate upper/lower bounding similarities iteratively, and (2) prune unnecessary nodes dynamically to obtain top-k nodes in each iteration. Experiments show that our approach is much faster than existing approaches.
Year
DOI
Venue
2013
10.1145/2463676.2463717
SIGMOD Conference
Keywords
Field
DocType
personalized pagerank,similarities iteratively,efficient ad-hoc search,approximate computation,interactive similarity search,ppr parameter,key castanet operation,high computation cost,previous ppr,top-k node,interactive action,graph database
PageRank,Graph,Data mining,Graph database,Ranking,Precomputation,Computer science,Theoretical computer science,Nearest neighbor search,Bounding overwatch,Computation
Conference
Citations 
PageRank 
References 
22
0.75
29
Authors
5
Name
Order
Citations
PageRank
Yasuhiro Fujiwara129225.43
Makoto Nakatsuji222513.16
Hiroaki Shiokawa315312.82
Takeshi Mishima4413.56
Makoto Onizuka536933.84