Estimating the dynamics of kernel-based evolving networks

  • Gábor Csárdi
  • Katherine Strandburg
  • László Zalányi
  • Jan Tobochnik
  • Péter érdi


In this paper we present the application of a novel methodology to scientific citation and collaboration networks. This methodology is designed for understanding the governing dynamics of evolving networks and relies on an attachment kernel, a scalar function of node properties, that stochastically drives the addition and deletion of vertices and edges. We illustrate how the kernel function of a given network can be extracted from the history of the network and discuss other possible applications.


Kernel Function Physical Review Preferential Attachment Collaboration Network Citation Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer 2010

Authors and Affiliations

  • Gábor Csárdi
    • 1
    • 2
  • Katherine Strandburg
    • 3
  • László Zalányi
    • 2
  • Jan Tobochnik
    • 4
  • Péter érdi
    • 1
  1. 1.Center for Complex Systems StudiesKalamazooUSA
  2. 2.Department of BiophysicsKFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of SciencesBudapestHungary
  3. 3.DePaul University — College of LawChicagoUSA
  4. 4.Department of Physics and Center for Complex Systems StudiesKalamazoo CollegeKalamazooUSA

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