Ranking influential spreaders is an ill-defined problem

@article{Gu2017RankingIS,
  title={Ranking influential spreaders is an ill-defined problem},
  author={Jain Gu and Sungmin Lee and Jari Saram{\"a}ki and Petter Holme},
  journal={ArXiv},
  year={2017},
  volume={abs/1703.05644}
}
Finding influential spreaders of information and disease in networks is an important theoretical problem, and one of considerable recent interest. It has been almost exclusively formulated as a node-ranking problem —methods for identifying influential spreaders output a ranking of the nodes. In this work, we show that such a greedy heuristic does not necessarily work: the set of most influential nodes depends on the number of nodes in the set. Therefore, the set of n most important nodes to… 
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Background Citations
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Methods Citations
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14 Citations

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References

SHOWING 1-10 OF 24 REFERENCES
Influence maximization in complex networks through optimal percolation
TLDR
This work maps the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network.
Benchmarking Measures of Network Influence
TLDR
It is found that none of the network measures applied to the induced flat graphs are accurate predictors of network propagation influence on the systems studied; however, temporal networks and the TKO measure provide the requisite targets for the search for effective predictive measures.
Locating influential nodes via dynamics-sensitive centrality
TLDR
A dynamics-sensitive (DS) centrality is presented by integrating topological features and dynamical properties and is more accurate than degree, k-shell index and eigenvector centrality.
Identification of influential spreaders in complex networks
Spreading of information, ideas or diseases can be conveniently modelled in the context of complex networks. An analysis now reveals that the most efficient spreaders are not always necessarily the…
Vital nodes identification in complex networks
Immunization and targeted destruction of networks using explosive percolation
TLDR
Tests on several real-world and model networks suggest that the method is more efficient and faster than any existing immunization strategy and because of the latter property it can deal with very large networks.
Networks and epidemic models
TLDR
A variety of methods are described that allow the mixing network, or an approximation to the network, to be ascertained and how the two fields of network theory and epidemiological modelling can deliver an improved understanding of disease dynamics and better public health through effective disease control are suggested.
Networks: An Introduction
TLDR
This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.
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.
Mathematics of Epidemics on Networks: From Exact to Approximate Models
This textbook provides an exciting new addition to the area of network science featuring a stronger and more methodical link of models to their mathematical origin and explains how these relate to…
...
...