# 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…

## 82 Citations

Spreading to localized targets in complex networks

- Computer Science, MedicineScientific reports
- 2016

Simulation results show that the proposed reversed local path algorithm outperforms the existing methods in identifying the influential nodes with respect to localized targets in complex networks and can effectively avoid infecting the non-target nodes in the spreading process.

The spreading ability of nodes towards localized targets in complex networks

- Computer Science, PhysicsArXiv
- 2015

Simulation results show that the reversed local path algorithm outperforms the existing methods in identifying the influential nodes with respect to these localized targets and the influential spreaders identified by the method can effectively avoid infecting the non-target nodes in the spreading process.

Leveraging percolation theory to single out influential spreaders in networks

- Mathematics, Computer SciencePhysical review. E
- 2016

It is proved that the recently introduced nonbacktracking centrality is the optimal criterion for the identification of influential spreaders in locally tree-like networks at criticality and is a highly reliable metric to identify top influential spreader also in generic graphs not embedded in space and for noncritical spreading.

Spreading dynamics in complex networks

- Computer Science, PhysicsArXiv
- 2013

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.

Identifying an influential spreader from a single seed in complex networks via a message-passing approach

- 2017

Abstract
Identifying the most influential spreaders is one of outstanding problems in physics of complex systems. So far, many approaches have attempted to rank the influence of nodes but there is…

Accurate prediction of influential spreaders in complex networks via message-passing approach

- Physics, Computer ScienceArXiv
- 2017

The problem of finding important spreaders is directly tackled by solving analytically the expected size of epidemic outbreaks when spreading originates from a single seed using message-passing approach and it is shown that the approach can be successfully adapted into weighted networks.

Identifying Top-K Important Nodes Based on Probabilistic-Jumping Random Walk in Complex Networks

- Computer ScienceCOMPLEX NETWORKS
- 2017

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.

Dynamic communicability and epidemic spread: a case study on an empirical dynamic contact network

- Computer Science, MathematicsJ. Complex Networks
- 2017

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.

Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks

- Physics, MedicinePloS one
- 2013

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.

Using LTI Dynamics to Identify the Influential Nodes in a Network

- Computer Science, MedicinePloS one
- 2016

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.

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