Isolation and connectivity in random geometric graphs with self-similar intensity measures

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)
328 Downloads (Pure)


Random geometric graphs consist of randomly distributed nodes (points), with pairs of nodes within a given mutual distance linked. In the usual model the distribution of nodes is uniform on a square, and in the limit of infinitely many nodes and shrinking linking range, the number of isolated nodes is Poisson distributed, and the probability of no isolated nodes is equal to the probability the whole graph is connected. Here we examine these properties for several self-similar node distributions, including smooth and fractal, uniform and nonuniform, and finitely ramified or otherwise. We show that nonuniformity can break the Poisson distribution property, but it strengthens the link between isolation and connectivity. It also stretches out the connectivity transition. Finite ramification is another mechanism for lack of connectivity. The same considerations apply to fractal distributions as smooth, with some technical differences in evaluation of the integrals and analytical arguments.
Original languageEnglish
Pages (from-to)679-700
Number of pages22
JournalJournal of Statistical Physics
Issue number3
Early online date16 May 2018
Publication statusPublished - Aug 2018


  • Random geometric graph
  • Degree distribution
  • Connectivity
  • Fractals


Dive into the research topics of 'Isolation and connectivity in random geometric graphs with self-similar intensity measures'. Together they form a unique fingerprint.

Cite this