Improving Distributed Resource Search through a Statistical Methodology of Topological Feature Selection

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.