Probabilistic Prediction in Scale-Free Networks: Diameter Changes

J. H. Kim, K. I. Goh, B. Kahng, D. Kim

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


In complex systems, responses to small perturbations are too diverse to definitely predict how much they would be, and then such diverse responses can be predicted in a probabilistic way. Here we study such a problem in scale-free networks, for example, the diameter changes by the deletion of a single vertex for various in silico and real-world scale-free networks. We find that the diameter changes are indeed diverse and their distribution exhibits an algebraic decay with an exponent [Formula presented] asymptotically. Interestingly, the exponent [Formula presented] is robust as [Formula presented] for most scale-free networks and insensitive to the degree exponents [Formula presented] as long as [Formula presented]. However, there is another type with [Formula presented] and its examples include the Internet and its related in silico model.

Original languageEnglish
JournalPhysical review letters
Issue number5
Publication statusPublished - 2003 Aug 1
Externally publishedYes

ASJC Scopus subject areas

  • General Physics and Astronomy


Dive into the research topics of 'Probabilistic Prediction in Scale-Free Networks: Diameter Changes'. Together they form a unique fingerprint.

Cite this