An extension of Matthews' bound to multiplex random walks

Yusuke Hosaka, Yukiko Yamauchi, Shuji Kijima, Hirotaka Ono, Masafumi Yamashita

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Random walk is a powerful tool for searching a network, especially for a very large network such as the Internet. The cover time is an important measure of a random walk on a finite graph, and has been studied well. For the purpose of searching a network, it is quite natural to think that multiple crawlers might cover a network faster than a single crawler. Alon et al. (2011) showed that a multiple random walk by k crawlers covers a network k times faster than a single random walk in certain graphs such as complete graphs, random graphs, etc., while the speeding up ratio is limited only to log k times in other graphs such as cycles and paths. This paper investigates a multiplex random walk by k tokens, in which k tokens randomly walks on a graph independently according to an individual transition probability. For the cover time of a multiplex random walk, we present new inequalities similar to celebrated Matthews' inequalities for a single random walk, that provide upper and lower bounds of the cover time by its hitting times. We also show that the bounds are tight in certain graphs, namely complete graphs, bipartite complete graphs, and random graphs.

本文言語英語
ホスト出版物のタイトルProceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
ページ872-877
ページ数6
DOI
出版ステータス出版済み - 10 18 2012
イベント2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012 - Shanghai, 中国
継続期間: 5 21 20125 25 2012

出版物シリーズ

名前Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012

その他

その他2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012
Country中国
CityShanghai
Period5/21/125/25/12

All Science Journal Classification (ASJC) codes

  • Software

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