Exponential random graph models for networks resilient to targeted attacks

Jingfei Zhang, Yuguo Chen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

One important question for complex networks is how the network's connectivity will be affected if the network is under targeted attacks, i.e., the nodes with the most links are attacked. In this paper, we fit an exponential random graph model to a dolphin network which is known to be resilient to targeted attacks. The fitted model characterizes network resiliency and identifies local structures that can reproduce the global resilience property. Such a statistical model can be used to build the Internet and other networks to increase the attack tolerance of those networks.

Original languageEnglish (US)
Pages (from-to)267-276
Number of pages10
JournalStatistics and its Interface
Volume8
Issue number3
DOIs
StatePublished - Jan 1 2015
Externally publishedYes

Fingerprint

Graph Model
Random Graphs
Attack
Complex networks
Internet
Resiliency
Network Connectivity
Resilience
Local Structure
Complex Networks
Statistical Model
Tolerance
Vertex of a graph
Statistical Models

Keywords

  • Exponential random graph model
  • Global efficiency
  • Markov chain Monte Carlo
  • Maximum likelihood estimation
  • Network robustness
  • Random graphs

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

Cite this

Exponential random graph models for networks resilient to targeted attacks. / Zhang, Jingfei; Chen, Yuguo.

In: Statistics and its Interface, Vol. 8, No. 3, 01.01.2015, p. 267-276.

Research output: Contribution to journalArticle

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