Bias reduction in traceroute sampling: towards a more accurate map of the Internet

TL;DR

This paper introduces a new estimator to reduce bias in traceroute sampling, enabling more accurate mapping of the internet's router and autonomous system graphs by correcting degree distribution estimates.

Contribution

It develops and validates a novel estimator for node degree in traceroute-sampled graphs, improving the accuracy of internet topology measurements.

Findings

The estimator reduces bias in degree distribution estimates.

Validation confirms the estimator's effectiveness in Erdos-Renyi and other graphs.

Application reveals a more accurate degree distribution of the autonomous system graph.

Abstract

Traceroute sampling is an important technique in exploring the internet router graph and the autonomous system graph. Although it is one of the primary techniques used in calculating statistics about the internet, it can introduce bias that corrupts these estimates. This paper reports on a theoretical and experimental investigation of a new technique to reduce the bias of traceroute sampling when estimating the degree distribution. We develop a new estimator for the degree of a node in a traceroute-sampled graph; validate the estimator theoretically in Erdos-Renyi graphs and, through computer experiments, for a wider range of graphs; and apply it to produce a new picture of the degree distribution of the autonomous system graph.