Exponential random graph models for networks resilient to targeted attacks

Jingfei Zhang, Yuguo Chen

Research output: Contribution to journalArticlepeer-review


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
Issue number3
StatePublished - 2015


  • 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


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