DisNet: A General Framework for Dissolving Networks

The universal presence of networks makes them an important conduit to study interactions in complex natural and artificial systems. While maintaining their integrity is crucial, in many cases, we are also interested in disconnecting them for disease prevention and control, failure containment, crime disruption, etc. With an array of methods exploring node importance, localized execution, and measurement of fragmentation, the choices we have can be disorienting. In this work, we propose a general framework, DisNet, in order to investigate the choice of node centralities and how distributed information gathering and decision making can help us achieve the balance between efficacy and cost of doing so. The framework was evaluated using computer simulation of network dissolution for the full process of weakening, breaking, and shattering. Measurements of focus include the structural losses such as increased effective diameter, homogenization of node degrees, and Shannon diversity of resultant network fragments.