| Internet topology characterization research is the necessary process of understanding Internet, and it is the base for a more deeply development of Internet. It is not only a problem of computer science, but there is a lack of scientific frame class for a large scale real network. This paper bases on complex network theory which is developing fast recently, and it researches topology characteristics of large scale IP networks by the numbers.Complex network research originated from graphical theory and stastical mechanics, and one of its main goal is to topologically characterize researching structures. This paper introduces complex network graphical theoretical characterizing methods, and analyzes many measurements, including degree distributing and correlation coefficients, clustering coefficients, coreness, betweenness, community identification and measurements. This paper classifies these measurements into two kinds: degree and path correlative measurements.According to the definition of these measurements, this paper presents many applications for different main characters, including degree distributing entropy, assortative coefficients, transitivity, betweenness distributing. This paper recommends an accelerate algorithm according to graph's own property, and this method can reduce 35%~54% computational consume.This paper uses above algorithms and applications analyzing topological characteristics for IP level of China network and Internet, and finds a lot of topological internal rules. After a general comparison to CAIDA's skitter data, we find that the skitter data loses many marginal network nodes, and most of characteristics are similar in qualitative analysis, but they are quantificationally different, especially distance measurements. Introducing measurement correlation analyzing techniques, this paper analyzes relativity of five main measurements, and finds low parts of node degree are correlative straight to others, and the high parts are less associated. |
