DMCA
Fast single-pair simrank computation (2010)
| Venue: | In Proc. of the SIAM Intl. Conf. on Data Mining (SDM2010 |
| Citations: | 11 - 2 self |
BibTeX
@INPROCEEDINGS{Li10fastsingle-pair,
author = {Pei Li and Hongyan Liu and Jeffrey Xu and Yu Jun and He Xiaoyong Du},
title = {Fast single-pair simrank computation},
booktitle = {In Proc. of the SIAM Intl. Conf. on Data Mining (SDM2010},
year = {2010}
}
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Abstract
SimRank is an intuitive and effective measure for link-based similarity that scores similarity between two nodes as the first-meeting probability of two random surfers, based on the random surfer model. However, when a user queries the similarity of a given node-pair based on SimRank, the existing approaches need to compute the similarities of other node-pairs beforehand, which we call an all-pair style. In this paper, we propose a Single-Pair SimRank approach. Without accuracy loss, this approach performs an iterative computation to obtain the similarity of a single node-pair. The time cost of our Single-Pair SimRank is always less than All-Pair SimRank and obviously efficient when we only need to assess similarity of one or a few node-pairs. We confirm the accuracy and efficiency of our approach in extensive experimental studies over synthetic and real datasets. 1
Keyphrases
single-pair simrank computation random surfer model extensive experimental study effective measure time cost real datasets random surfer single node-pair single-pair simrank all-pair simrank node-pairs beforehand single-pair simrank approach accuracy loss link-based similarity iterative computation first-meeting probability all-pair style
