Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: A walk counting approach

@article{Bauer2012IdentifyingIS,
  title={Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: A walk counting approach},
  author={Frank Bauer and Joseph T. Lizier},
  journal={EPL},
  year={2012},
  volume={99},
  pages={68007}
}
We introduce a new method to efficiently approximate the number of infections resulting from a given initially infected node in a network of susceptible individuals. Our approach is based on counting the number of possible infection walks of various lengths to each other node in the network. We analytically study the properties of our method, in particular demonstrating different forms for SIS and SIR disease spreading (e.g., under the SIR model our method counts self-avoiding walks). In… Expand
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81 Citations
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Background Citations
18
Methods Citations
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29 October 2012

81 Citations

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Citation Type

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Some of the most important theoretical models that describe spreading processes are displayed, and the problem of locating both the individual and multiple influential spreaders respectively are discussed. Expand
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Experimental results suggest that the PJRW method can effectively find the important top-k nodes and its result in most networks is better than degree, betweenness, closeness and eigenvector centrality. Expand
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It is shown that temporal centrality identifies a distinct set of top-spreaders than centrality based on the time-aggregated binarized contact matrix, so that taken together, the accuracy of capturing top- spreaders improves significantly and the temporal measure does not necessarily outperform less complex measures. Expand
Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks
TLDR
A new measure is proposed that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states, that can be extended to include random walk based definitions and its computational complexity is shown to be of the same order as that of betweenness centrality. Expand
Using LTI Dynamics to Identify the Influential Nodes in a Network
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The Node Imposed Response (NiR), a measure which accurately evaluates node spreading power, is proposed which outperforms betweenness, degree, k-shell and h-index centrality in many cases and shows the similar accuracy to dynamics-sensitive centrality. Expand
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