TCP/IP illustrated (vol. 1): the protocols
TCP/IP illustrated (vol. 1): the protocols
Self-Organizing Maps
Linked
Should we build Gnutella on a structured overlay?
ACM SIGCOMM Computer Communication Review
FIRST-PASSAGE PERCOLATION ON THE RANDOM GRAPH
Probability in the Engineering and Informational Sciences
On the bias of traceroute sampling: or, power-law degree distributions in regular graphs
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Performance Analysis of Communications Networks and Systems
Performance Analysis of Communications Networks and Systems
The internet AS-level topology: three data sources and one definitive metric
ACM SIGCOMM Computer Communication Review
Connectivity in wireless ad-hoc networks with a log-normal radio model
Mobile Networks and Applications
Size and Weight of Shortest Path Trees with Exponential Link Weights
Combinatorics, Probability and Computing
Discarte: a disjunctive internet cartographer
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
The observable part of a network
IEEE/ACM Transactions on Networking (TON)
| Hi-index | 0.00 |
Many network topology measurements capture or sample only a partial view of the actual network structure, which we call the underlying network. Sampling bias is a critical problem in the field of complex networks ranging from biological networks, social networks and artificial networks like the Internet. This bias phenomenon depends on both the sampling method of the measurements and the features of the underlying networks. In RIPE NCC and the PlanetLab measurement architectures, the Internet is mapped as G"@?"""m"s"p"t, the union of shortest paths between each pair of a set M of m testboxes, or equivalently, m shortest path trees. In this paper, we investigate this sampling method on a wide class of real-world complex networks as well as on the weighted Erdos-Renyi random graphs. This general framework examines the effect of the set of testboxes on G"@?"""m"s"p"t. We establish the correlation between the subgraph G"M of the underlying network, i.e. the set M and the direct links between nodes of set M, and the sampled network G"@?"""m"s"p"t. Furthermore, we illustrate that in order to obtain an increasingly accurate view of a given network, a higher than linear detection/measuring effort (the relative size m/N of set M) is needed, where N is the size of the underlying network. Finally, when the relative size m/N of set M is small, we characterize the kind of networks possessing small sampling bias, which provides insights on how to place the testboxes for good network topology measurement.