ISSN : 1796-203X
Volume : 4    Issue : 8    Date : August 2009

Improving Distributed Resource Search through a Statistical Methodology of Topological Feature Selection
Claudia Gómez Santillán, Laura Cruz-Reyes, Eustorgio Meza, Tania Turrubiates López, Marco A. Aguirre Lam,
and Elisa Schaeffer
Page(s): 727-733
Full Text:
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The Internet is considered a complex network for its size, interconnectivity and rules that govern are dynamic,
because of constantly evolve. For this reason the search of distributed resources shared by users and online
communities is a complex task that needs efficient search method. The goal of this work is to improve the
performance of distributed search of information, through analysis of the topological features. In this paper we
described a statistical methodology to select a set of topologic metrics that allow to locally distinguish the type
of complex network. In this way we use the metrics to guide the search towards nodes with better connectivity.
In addition we present an algorithm for distributed search of information, enriched with the selected topological
metric. The results show that including the topological metric in the Neighboring-Ant Search algorithm
improves its performance 50% in terms of the number of hops needed to locate a set of resources. The
methodology described provides a better understanding of why the features were selected and aids to explain
how this metric impacts in the search process.

Index Terms
Internet, search process, query routing, random walk, ant colony system, scale free, topology, experiment
designs, statistical analysis, metrics