Scott Duxbury

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  Attack Tolerance of Dynamic Networks
  Scott Duxbury

  Interconnected systems in physical, biological, and social sciences are often at risk of attacks from exogenous sources. As a result, a growing number of studies focus on network attack tolerance. In general, this body of research assumes that damage to cross-sectional networks persists over time and has almost ubiquitously focused on attack strategies targeting integral vertices or edges. At issue, however, is that many networks are dynamic, especially in the social sciences, and capable of adaptive responses to attacks. Relatedly, network attack strategies may be diffuse, targeting an array of weak links, rather than high profile vertices. Together, these two omissions limit researchers’ ability to reach firm conclusions or derive policy recommendations from past research. Expanding on this prior work, we examine data collected from an online drug trafficking network comprised of roughly 7,400 actors and 17,000 illicit drug transactions observed over 14 months. We use these data to develop an agent-based simulation experiment evaluating how the drug trafficking network responds to targeted and diffuse attacks. Results show that the network suffers substantial damage from diffuse attacks and that conventional methods for evaluating network robustness do a poor job of representing this type of damage. In particular, cross-sectional measures of network robustness in the simulated output networks suggest that the networks actually grow stronger in the aftermath of a diffuse attack, despite losing a substantial portion of edges and vertices. Pertinent to policy, these results indicate that the diffuse attack strategy evaluated in this study is an effective tactic for curbing online drug trafficking—an issue which has vexed law enforcement for some time.