On power-law relationships of the Internet topology

TLDR

The power‑laws hold across three Internet snapshots from November 1997 to December 1998, despite a 45 % growth, and concisely describe skewed distributions of graph properties such as node outdegree. The authors aim to use the power‑laws to estimate key parameters like average neighborhood size, aid protocol design and performance analysis, and generate realistic topologies for simulation. They generate realistic network topologies for simulation by applying the power‑laws. The authors find that simple power‑laws fit Internet topology data with correlation coefficients above 96 %, offering a novel perspective and enabling estimation of parameters such as average neighborhood size.

Abstract

Despite the apparent randomness of the Internet, we discover some surprisingly simple power-laws of the Internet topology. These power-laws hold for three snapshots of the Internet, between November 1997 and December 1998, despite a 45% growth of its size during that period. We show that our power-laws fit the real data very well resulting in correlation coefficients of 96% or higher.Our observations provide a novel perspective of the structure of the Internet. The power-laws describe concisely skewed distributions of graph properties such as the node outdegree. In addition, these power-laws can be used to estimate important parameters such as the average neighborhood size, and facilitate the design and the performance analysis of protocols. Furthermore, we can use them to generate and select realistic topologies for simulation purposes.