Statistical Models for Networks Resilient to Targeted Attacks

• Activity Number: 696
• Type: Contributed
• Date/Time: Thursday, August 8, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Social Statistics Section
• Author(s): Jingfei Zhang*+ and Yuguo Chen
• Companies: University of Illinois and University of Illinois at Urbana-Champaign
• Keywords: Exponential random graph model ; Global efficiency ; Markov chain Monte Carlo ; Maximum likelihood estimation ; Network robustness ; Random graphs

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 social networks to increase the attack tolerance of those networks.