Network Topology Generators: Degree-Based vs. Structural. Tangmunarunkit, H., Govindan, R., Jamin, S., Shenker, S., & Willinger, W. SIGCOMM Comput. Commun. Rev., 32(4):147–159, Association for Computing Machinery, New York, NY, USA, aug, 2002.
Network Topology Generators: Degree-Based vs. Structural [link]Paper  doi  abstract   bibtex   
Following the long-held belief that the Internet is hierarchical, the network topology generators most widely used by the Internet research community, Transit-Stub and Tiers, create networks with a deliberately hierarchical structure. However, in 1999 a seminal paper by Faloutsos et al. revealed that the Internet's degree distribution is a power-law. Because the degree distributions produced by the Transit-Stub and Tiers generators are not power-laws, the research community has largely dismissed them as inadequate and proposed new network generators that attempt to generate graphs with power-law degree distributions.Contrary to much of the current literature on network topology generators, this paper starts with the assumption that it is more important for network generators to accurately model the large-scale structure of the Internet (such as its hierarchical structure) than to faithfully imitate its local properties (such as the degree distribution). The purpose of this paper is to determine, using various topology metrics, which network generators better represent this large-scale structure. We find, much to our surprise, that network generators based on the degree distribution more accurately capture the large-scale structure of measured topologies. We then seek an explanation for this result by examining the nature of hierarchy in the Internet more closely; we find that degree-based generators produce a form of hierarchy that closely resembles the loosely hierarchical nature of the Internet.
@article{tangmunarunkit_sigcomm_2002,
  author = {Tangmunarunkit, Hongsuda and Govindan, Ramesh and Jamin, Sugih and Shenker, Scott and Willinger, Walter},
  title = {Network Topology Generators: Degree-Based vs. Structural},
  year = {2002},
  issue_date = {October 2002},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {32},
  number = {4},
  issn = {0146-4833},
  url = {https://doi.org/10.1145/964725.633040},
  doi = {10.1145/964725.633040},
  abstract = {Following the long-held belief that the Internet is hierarchical, the network topology generators most widely used by the Internet research community, Transit-Stub and Tiers, create networks with a deliberately hierarchical structure. However, in 1999 a seminal paper by Faloutsos et al. revealed that the Internet's degree distribution is a power-law. Because the degree distributions produced by the Transit-Stub and Tiers generators are not power-laws, the research community has largely dismissed them as inadequate and proposed new network generators that attempt to generate graphs with power-law degree distributions.Contrary to much of the current literature on network topology generators, this paper starts with the assumption that it is more important for network generators to accurately model the large-scale structure of the Internet (such as its hierarchical structure) than to faithfully imitate its local properties (such as the degree distribution). The purpose of this paper is to determine, using various topology metrics, which network generators better represent this large-scale structure. We find, much to our surprise, that network generators based on the degree distribution more accurately capture the large-scale structure of measured topologies. We then seek an explanation for this result by examining the nature of hierarchy in the Internet more closely; we find that degree-based generators produce a form of hierarchy that closely resembles the loosely hierarchical nature of the Internet.},
  journal = {SIGCOMM Comput. Commun. Rev.},
  month = {aug},
  pages = {147–159},
  numpages = {13},
  keywords = {degree-based generators, hierarchy, topology generators, topology metrics, network topology, large-scale structure, structural generators, topology characterization}
}
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