Evolving graphs: dynamical models, inverse problems and propagation

Peter Grindrod, D.J. Higham

Research output: Contribution to journalArticle

65 Citations (Scopus)
143 Downloads (Pure)


Applications such as neuroscience, telecommunication, on-line social networking, transport and retail trading give rise to connectivity patterns that change over time. In this work we address the resulting need for network models and computational algorithms that deal with dynamic links. We introduce a new class of evolving range-dependent random graphs that gives a realistic but tractable framework for modeling and simulation. We develop a spectral algorithm for calibrating a set of edge ranges from a sequence of network snapshots, and give a proof of principle illustration on some neuroscience data. We also show how the model can be used computationally and analytically to investigate the scenario where an evolutionary process, such as an epidemic, takes place on an evolving network. This allows us to study the cumulative effect of two distinct types of dynamics.
Original languageEnglish
Pages (from-to)753-770
Number of pages18
JournalProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2115
Publication statusPublished - 8 Mar 2010


  • birth and death process
  • epidemiology
  • network
  • neuroscience
  • random graph
  • reproduction rate


Dive into the research topics of 'Evolving graphs: dynamical models, inverse problems and propagation'. Together they form a unique fingerprint.

Cite this