A statistical approach to the traceroute-like exploration of networks: theory and simulations

TL;DR

This paper analyzes the biases in network mapping via traceroute-like probes, providing a statistical framework that relates sampling accuracy to network topology and exploring strategies to optimize network discovery.

Contribution

It introduces a mean-field analytical model for sampling biases, linking detection probabilities to betweenness centrality, and evaluates sampling strategies through simulations.

Findings

Detection probability depends on betweenness centrality.

Shortest path sampling better characterizes scale-free networks.

Optimal exploration parameters improve node and edge discovery.

Abstract

Mapping the Internet generally consists in sampling the network from a limited set of sources by using "traceroute"-like probes. This methodology, akin to the merging of different spanning trees to a set of destinations, has been argued to introduce uncontrolled sampling biases that might produce statistical properties of the sampled graph which sharply differ from the original ones. Here we explore these biases and provide a statistical analysis of their origin. We derive a mean-field analytical approximation for the probability of edge and vertex detection that exploits the role of the number of sources and targets and allows us to relate the global topological properties of the underlying network with the statistical accuracy of the sampled graph. In particular we find that the edge and vertex detection probability is depending on the betweenness centrality of each element. This allows us to show that shortest path routed sampling provides a better characterization of underlying graphs with scale-free topology. We complement the analytical discussion with a throughout numerical investigation of simulated mapping strategies in different network models. We show that sampled graphs provide a fair qualitative characterization of the statistical properties of the original networks in a fair range of different strategies and exploration parameters. The numerical study also allows the identification of intervals of the exploration parameters that optimize the fraction of nodes and edges discovered in the sampled graph. This finding might hint the steps toward more efficient mapping strategies.