Multilevel k-way partitioning scheme for irregular graphs
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The link prediction problem for social networks
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Automatic multimedia cross-modal correlation discovery
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Manifold-ranking based image retrieval
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Neighborhood Formation and Anomaly Detection in Bipartite Graphs
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The VLDB Journal — The International Journal on Very Large Data Bases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Fast algorithms for topk personalized pagerank queries
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Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Simrank++: query rewriting through link analysis of the click graph
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Proceedings of the VLDB Endowment
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
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Neighborhood based fast graph search in large networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Quick detection of top-k personalized pagerank lists
WAW'11 Proceedings of the 8th international conference on Algorithms and models for the web graph
Efficient personalized pagerank with accuracy assurance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient ad-hoc search for personalized PageRank
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
IRWR: incremental random walk with restart
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
LR-PPR: locality-sensitive, re-use promoting, approximate personalized pagerank computation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Graphs are fundamental data structures and have been employed for centuries to model real-world systems and phenomena. Random walk with restart (RWR) provides a good proximity score between two nodes in a graph, and it has been successfully used in many applications such as automatic image captioning, recommender systems, and link prediction. The goal of this work is to find nodes that have top-k highest proximities for a given node. Previous approaches to this problem find nodes efficiently at the expense of exactness. The main motivation of this paper is to answer, in the affirmative, the question, 'Is it possible to improve the search time without sacrificing the exactness?'. Our solution, K-dash, is based on two ideas: (1) It computes the proximity of a selected node efficiently by sparse matrices, and (2) It skips unnecessary proximity computations when searching for the top-k nodes. Theoretical analyses show that K-dash guarantees result exactness. We perform comprehensive experiments to verify the efficiency of K-dash. The results show that K-dash can find top-k nodes significantly faster than the previous approaches while it guarantees exactness.