Ranking the spreading influence in complex networks

Abstract

Identifying the node spreading influence in networks is an important task to optimally use the network structure and ensure the more efficient spreading in information. In this paper, by taking into account the shortest distance between a target node and the node set with the highest $k$-core value, we present an improved method to generate the ranking list to evaluate the node spreading influence. Comparing with the epidemic process results for four real networks and the Barabási-Albert network, the parameterless method could identify the node spreading influence more accurately than the ones generated by the degree $k$, closeness centrality, $k$-shell and mixed degree decomposition methods. This work would be helpful for deeply understanding the node importance of a network.

6 pages, 3 figures

References in corpus (8)

• Reaction-diffusion processes and metapopulation models in heterogeneous networks
• Cascade control and defense in complex networks
• New Model of Internet Topology Using k-shell Decomposition
• Thresholds for epidemic spreading in networks
• Ranking spreaders by decomposing complex networks
• Graph Evolution: Densification and Shrinking Diameters
• Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity
• Heat Conduction Process on Community Networks as a Recommendation Model

Cited by in corpus (4)

• Vital nodes identification in complex networks
• Centrality measure based on continuous-time quantum walks and experimental realization
• Promotion and resignation in employee networks
• Improving the accuracy of the k-shell method by removing redundant links-from a perspective of spreading dynamics