Selection in scale-free small world

Zsolt Palotai, Csilla Farkas, András Lorincz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this paper we compare our selection based learning algorithm with the reinforcement learning algorithm in Web crawlers. The task of the crawlers is to find new information on the Web. We performed simulations based on data collected from the Web. The collected portion of the Web is typical and exhibits scale-free small world (SFSW) structure. We have found that on this SFSW, the weblog update algorithm performs better than the reinforcement learning algorithm. It finds the new information faster than the reinforcement learning algorithm and has better new information/all submitted documents ratio.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages579-582
Number of pages4
DOIs
Publication statusPublished - Dec 1 2005
Event4th International Central and Eastern European Conference on Multi-Agent Systems, CEEMAS 2005 - Budapest, Hungary
Duration: Sep 15 2005Sep 17 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3690 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Central and Eastern European Conference on Multi-Agent Systems, CEEMAS 2005
CountryHungary
CityBudapest
Period9/15/059/17/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Palotai, Z., Farkas, C., & Lorincz, A. (2005). Selection in scale-free small world. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 579-582). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3690 LNAI). https://doi.org/10.1007/11559221_65