Simple contagion processes underlie various phenomena on complex networks, such as the spread of diseases on social-contact networks and information in communication networks; understanding their dynamics and developing control mechanisms are key issues in numerous applications. The goals of this proposal are: (i) Developing methods to construct synthetic relational networks using partial and noisy data; (ii) Understanding the structure of these networks and the contagion processes, and especially important network properties and typical patterns that have an impact on the dynamics of contagion; (iii) Developing techniques to control the spread of contagion processes, and to detect, prevent and arrest cascading failures in coupled socio-technical networks; and (iv) Understanding the co-evolution between the networks and dynamics, and using this to refine their models, and the strategies to control them. The broader impacts of this work include bridging the gap between the social sciences and computer science in addressing fundamental questions in complex networks, a corresponding enhancement to course curricula, and the involvement of students at all levels.
