Selection in Scale-Free Small World

TL;DR

This paper compares a selection-based learning algorithm with reinforcement learning for Web crawlers, demonstrating that the selection method finds new information faster and more efficiently on scale-free small world Web structures.

Contribution

It introduces and evaluates a selection-based learning algorithm for Web crawlers, showing its superior performance over reinforcement learning in scale-free small world environments.

Findings

Selection algorithm outperforms reinforcement learning in speed.

Selection algorithm has higher ratio of new information found.

Performance advantage attributed to Web's small world structure.

Abstract

In this paper we compare the performance characteristics of our selection based learning algorithm for Web crawlers with the characteristics of the reinforcement learning algorithm. The task of the crawlers is to find new information on the Web. The selection algorithm, called weblog update, modifies the starting URL lists of our crawlers based on the found URLs containing new information. The reinforcement learning algorithm modifies the URL orderings of the crawlers based on the received reinforcements for submitted documents. 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. We believe that the advantages of the selection algorithm over reinforcement learning algorithm is due to the small world property of the Web.