On the bias of traceroute sampling: or, power-law degree distributions in regular graphs

Understanding the graph structure of the Internet is a crucial step for building accurate network models and designing efficient algorithms for Internet applications. Yet, obtaining this graph structure can be a surprisingly difficult task, as edges cannot be explicitly queried. For instance, empirical studies of the network of Internet Protocol (IP) addresses typically rely on indirect methods like traceroute to build what are approximately single-source, all-destinations, shortest-path trees. These trees only sample a fraction of the network's edges, and a recent paper by Lakhina et al. found empirically that the resulting sample is intrinsically biased. Further, in simulations, they observed that the degree distribution under traceroute sampling exhibits a power law even when the underlying degree distribution is Poisson. In this paper, we study the bias of traceroute sampling mathematically and, for a very general class of underlying degree distributions, explicitly calculate...

• Type: Conference
• Year: 2005
• Where: STOC
• Authors: Dimitris Achlioptas, Aaron Clauset, David Kempe, Cristopher Moore
• Keywords: Algorithms, Arbitrary Degree Distributions, STOC 2005, Traceroute Sampling Exhibits, Traceroute Sampling Finds