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={Europhysics Letters},
  year={2017},
  volume={118}
}
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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14 Citations
Background Citations
6
Methods Citations
2

14 Citations

Identifying Multiple Influential Spreaders in Complex Networks by Considering the Dispersion of Nodes
TLDR
The experimental results show that the proposed methods can identify nodes that are more influential, which suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders.
Identifying Multiple Influential Spreaders in Complex Networks by Considering the Dispersion of Nodes
TLDR
The experimental results show that the proposed methods can identify nodes that are more influential, which suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders.
Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities
TLDR
This work investigates how well the position of a node, characterized by standard network measures, can predict its epidemiological importance in any graph of a given number of nodes to find that combinations of two or more centralities are predictive even for the most difficult parameter values of the epidemic simulation.
Identifying influential nodes in heterogeneous networks
Three faces of node importance in network epidemiology: Exact results for small graphs
TLDR
It is found that node separation is more important than centrality for more than one active node, vaccination and influence maximization are the most different aspects of importance, and the three aspects are more similar when the infection rate is low.
Link transmission centrality in large-scale social networks
TLDR
A new measure based on stochastic diffusion processes, the transmission centrality, that captures the importance of links by estimating the average number of nodes to whom they transfer information during a global spreading diffusion process is proposed.
Influencers identification in complex networks through reaction-diffusion dynamics
TLDR
It is shown that the new centrality metric significantly outperforms state-of-the-art metrics in detecting the influencers for global contagion processes and reveals the essential role of the network effective distance for the influencer identification and lead to the optimal solution of the problem.
Dynamics of ranking
Virtually anything can be and is ranked; people, institutions, countries, words, genes. Rankings reduce complex systems to ordered lists, reflecting the ability of their elements to perform relevant…
Influencer identification in dynamical complex systems
TLDR
This review surveys recent advances in the study of influencer identification developed from different perspectives, and presents state-of-the-art solutions designed for different objectives.
Identification of influencers in online social networks: measuring influence considering multidimensional factors exploration
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