Name
Abstract | ||
|---|---|---|
Most data concerning the topology of complex networks are the result of mapping projects which bear intrinsic limitations and cannot give access to complete, unbiased datasets. A particularly interesting case is represented by the physical Internet. Router-level Internet mapping projects generally consist of sampling the network from a limited set of sources by using traceroute probes. This methodology, akin to the merging of spanning trees from the different sources to a set of destinations, leads necessarily to a partial, incomplete map of the Internet. The determination of the real Internet topology characteristics from such sampled maps is therefore, in part, a problem of statistical inference. In this paper we present a twofold contribution in order to address this problem. First, we argue that inference of some of the standard topological quantities is, in fact, a version of the so-called "species" problem in statistics, which is important in categorizing the problem and providing some indication of its inherent difficulties. Second, we tackle the issue of estimating arguably the most basic of network characteristics-its number of nodes-and propose two estimators for this quantity, based on subsampling principles. Numerical simulations, as well as an experiment based on probing the Internet, suggest the feasibility of accounting for measurement bias in reporting Internet topology characteristics. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1103/PhysRevE.75.056111 | PHYSICAL REVIEW E |
Keywords | Field | DocType |
internet topology,limit set,statistical inference,spanning tree | Internet topology,Network mapping,Data mining,Inference,Theoretical computer science,Spanning tree,Sampling (statistics),Statistical inference,Species problem,Mathematics,The Internet | Journal |
Volume | Issue | ISSN |
75 | 5 | 1539-3755 |
Citations | PageRank | References |
15 | 1.05 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabien Viger | 1 | 323 | 16.44 |
| Alain Barrat | 2 | 1401 | 87.12 |
| Luca Dall'Asta | 3 | 493 | 39.53 |
| Cun-Hui Zhang | 4 | 174 | 18.38 |
| Eric D. Kolaczyk | 5 | 169 | 11.39 |