Robustness of spatial preferential attachment networks

Emmanuel Jacob, Peter Morters

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding


We study robustness under random attack for a class of networks, in which new nodes are given a spatial position and connect to existing vertices with a probability favouring short spatial distances and high degrees. In this model of a scale-free network with clustering one can independently tune the power law exponent τ > 2 of the degree distribution and a parameter δ > 1 determining the decay rate of the probability of long edges. We argue that the network is robust if (Formula Presented.), but fails to be robust if (Formula Presented.). Hence robustness depends not only on the power-law exponent but also on the clustering features of the network.

Original languageEnglish
Title of host publicationAlgorithms and Models for the Web Graph
Subtitle of host publicationProceedings of 12th International Workshop, WAW 2015, Eindhoven, The Netherlands, December 10-11, 2015
EditorsD. F. Gleich, J. Komjathy, N. Litvak
Place of PublicationSwitzerland
Number of pages12
ISBN (Print)9783319267838
Publication statusPublished - 9 Dec 2015
Event12th International Workshop on Algorithms and Models for the Web Graph, WAW 2015 - Eindhoven, Netherlands
Duration: 10 Dec 201511 Dec 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


Conference12th International Workshop on Algorithms and Models for the Web Graph, WAW 2015


  • Barabasi-Albert model
  • Clustering
  • Geometric random graph
  • Giant component
  • Power law
  • Preferential attachment
  • Resilience
  • Robustness
  • Scale-free network


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