Three faces of node importance in network epidemiology: Exact results for small graphs

@article{Holme2017ThreeFO,
  title={Three faces of node importance in network epidemiology: Exact results for small graphs},
  author={Petter Holme},
  journal={Physical Review. E},
  year={2017},
  volume={96}
}
  • Petter Holme
  • Published 22 August 2017
  • Computer Science
  • Physical Review. E
We investigate three aspects of the importance of nodes with respect to susceptible-infectious-removed (SIR) disease dynamics: influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size), and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes). We calculate the exact expressions of these quantities, as functions of the SIR parameters, for all… 

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References

SHOWING 1-10 OF 52 REFERENCES
Ranking influential spreaders is an ill-defined problem
TLDR
This work shows that a greedy heuristic does not necessarily work: the set of most influential nodes depends on the number of nodes in the set, and proposes a method for quantifying the extent and impact of this phenomenon.
Cost-efficient vaccination protocols for network epidemiology
TLDR
This work investigates methods to vaccinate contact networks—i.e. removing nodes in such a way that disease spreading is hindered as much as possible—with respect to their cost-efficiency, and finds the so-called acquaintance vaccination is the most cost efficient.
Graphs with specified degree distributions, simple epidemics, and local vaccination strategies
Consider a random graph, having a prespecified degree distribution F, but other than that being uniformly distributed, describing the social structure (friendship) in a large community. Suppose that
Social Network Sensors for Early Detection of Contagious Outbreaks
TLDR
This paper proposes an alternative strategy that does not require ascertainment of global network structure, namely, simply monitoring the friends of randomly selected individuals, which could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks.
Fundamental difference between superblockers and superspreaders in networks
TLDR
An extensive analysis over a large set of real-world networks is performed to test the similarity between sets of superblockers and of superspreaders, and shows that the two optimization problems are not equivalent: superblocker do not act as optimal spreaders.
Maximizing the spread of influence through a social network
TLDR
An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
Optimizing surveillance for livestock disease spreading through animal movements
TLDR
This work characterizes livestock epidemics in terms of robust features useful for planning and control, to deal with temporal fluctuations, sensitivity to initial conditions and missing information during an outbreak, by focusing on the case study of cattle displacements in Italy.
Epidemic processes in complex networks
TLDR
A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear.
Directionality of contact networks suppresses selection pressure in evolutionary dynamics.
  • N. Masuda
  • Biology
    Journal of theoretical biology
  • 2009
...
...