Fundamental difference between superblockers and superspreaders in networks
Open Access
- 18 January 2017
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review E
- Vol. 95 (1) , 012318
Abstract
Two important problems regarding spreading phenomena in complex topologies are the optimal selection of node sets either to minimize or maximize the extent of outbreaks. Both problems are nontrivial when a small fraction of the nodes in the network can be used to achieve the desired goal. The minimization problem is equivalent to a structural optimization. The “superblockers,” i.e., the nodes that should be removed from the network to minimize the size of outbreaks, are those nodes that make connected components as small as possible. “Superspreaders” are instead the nodes such that, if chosen as initiators, they maximize the average size of outbreaks. The identity of superspreaders is expected to depend not just on the topology, but also on the specific dynamics considered. Recently, it has been conjectured that the two optimization problems might be equivalent, in the sense that superblockers act also as superspreaders. In spite of its potential groundbreaking importance, no empirical study has been performed to validate this conjecture. In this paper, we perform an extensive analysis over a large set of real-world networks to test the similarity between sets of superblockers and of superspreaders. We show that the two optimization problems are not equivalent: superblockers do not act as optimal spreaders.Keywords
All Related Versions
Funding Information
- National Science Foundation (CMMI-1552487)
- Army Research Office (W911NF-16-1-0104)
This publication has 25 references indexed in Scilit:
- Fast and simple decycling and dismantling of networksScientific Reports, 2016
- Immunization and Targeted Destruction of Networks using Explosive PercolationPhysical Review Letters, 2016
- Network dismantlingProceedings of the National Academy of Sciences, 2016
- Vital nodes identification in complex networksPhysics Reports, 2016
- Identifying optimal targets of network attack by belief propagationPhysical Review E, 2016
- Identify influential spreaders in complex networks, the role of neighborhoodPhysica A: Statistical Mechanics and its Applications, 2016
- Epidemic processes in complex networksReviews of Modern Physics, 2015
- Influence maximization in complex networks through optimal percolationNature, 2015
- Networks2010
- Complex networks: Structure and dynamicsPhysics Reports, 2006