A Comparative Analysis of Specific Spatial Network Topological Models

  • Jun Wang
  • Gregory Provan
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 5)


Creating ensembles of random but “realistic” topologies for complex systems is crucial for many tasks such as benchmark generation and algorithm analysis. In general, explanatory models are preferred to capture topologies of technological and biological complex systems, and some researchers claimed that it is largely impossible to capture any nontrivial network structure while ignoring domain-specific constraints. We study topology models of specific spatial networks, and show that a simple descriptive model, the generalized random graph model (GRG) which only reproduces the degree sequence of complex networks, can closely match the topologies of a variety of real-world spatial networks including electronic circuits, brain and neural networks and transportation networks, and outperform some plausible and explanatory models which consider spatial constraints.


Spatial Networks Random Graph Models 


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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2009

Authors and Affiliations

  • Jun Wang
    • 1
  • Gregory Provan
    • 1
  1. 1.Department of Computer ScienceUniversity College CorkIreland

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