The DOI matches its Crossref record.
A quantitative comparison of graph-based models for Internet topology
Abstract
Graphs are commonly used to model the topological structure of internetworks in order to study problems ranging from routing to resource reservation. A variety of graphs are found in the literature, including fixed topologies such as rings or stars, "well-known" topologies such as the ARPAnet, and randomly generated topologies. While many researchers rely upon graphs for analytic and simulation studies, there has been little analysis of the implications of using a particular model or how the graph generation method may affect the results of such studies. Further, the selection of one generation method over another is often arbitrary, since the differences and similarities between methods are not well understood. This paper considers the problem of generating and selecting graphs that reflect the properties of real internetworks. We review generation methods in common use and also propose several new methods. We consider a set of metrics that characterize the graphs produced by a method, and we quantify similarities and differences among several generation methods with respect to these metrics. We also consider the effect of the graph model in the context of a specific problem, namely multicast routing.
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Citations
Cited by
Error and attack tolerance of complex networks
Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534
Large-scale topological and dynamical properties of the Internet
Vázquez, A., Pastor-Satorras, R., & Vespignani, A. (2002) · Physical Review E · doi:10.1103/physreve.65.066130 · Cited by 712
Metadata
- Authors
- Zegura, E. W., Calvert, K. L., & Donahoo, M. J.
- Venue
- IEEE/ACM Transactions on Networking
- Published
- 1997
- Volume
- 5
- Issue
- 6
- Pages
- 770-783
- ISSN
- 1063-6692 (ISSN-L)
- OpenAlex
- W2142735084
- Cited by
- 482
- References
- 0
- FWCI
- 12.69
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.