The effective use of limited resources for controlling spreading processes on networks is of prime significance in diverse contexts, ranging from the identification of "influential spreaders" for maximizing information dissemination and targeted interventions in regulatory networks, to the development of mitigation policies for infectious diseases and financial contagion in economic systems. Solutions for these optimization tasks that are based purely on topological arguments are not fully satisfactory; in realistic settings the problem is often characterized by heterogeneous interactions and requires interventions over a finite time window via a restricted set of controllable nodes. The optimal distribution of available resources hence results from an interplay between network topology and spreading dynamics. We show how these problems can be addressed as particular instances of a universal analytical framework based on a scalable dynamic message-passing approach and demonstrate the efficacy of the method on a variety of real-world examples.
Bibliographical noteCopyright © 2017 National Academy of Sciences.
Funding: A.Y.L. was supported by Laboratory Directed Research and Development Program at Los Alamos National Laboratory by the National Nuclear Security Administration of the US Department of Energy under Contract DE-AC52-06NA25396. D.S. was supported by Leverhulme Trust Grant RPG-2013-48.
- Dynamic resource allocation
- Influence maximization
- Message-passing algorithms
- Mitigation of epidemic outbreak
- Optimal control of spreading processes