Selection in scale-free small world

Authors:
Zsolt Palotai;Csilla Farkas;András Lőrincz
Affiliations:
Department of Information Systems, Eötvös Loránd University, Budapest, Hungary;Department of Computer Sciences and Engineering, University of South Carolina, Columbia, SC;Department of Information Systems, Eötvös Loránd University, Budapest, Hungary
Venue:
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Year:
2005

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Using Reinforcement Learning to Spider the Web Efficiently

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning

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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.