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 (23)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• Error and attack tolerance of complex networks
• The spread of epidemic disease on networks
• Identification of influential spreaders in complex networks
• Self-similar community structure in organisations
• Dynamical and correlation properties of the Internet
• Breakdown of the Internet under intentional attack
• 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
• MEDUSA - New Model of Internet Topology Using k-shell Decomposition
• Thresholds for epidemic spreading in networks
• k-core organization of complex 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
• Absence of influential spreaders in rumor dynamics
• A measure of individual role in collective dynamics
• Competing activation mechanisms in epidemics on networks
• Information filtering via biased heat conduction
• Locating privileged spreaders on an Online Social Network

Cited by in corpus (21)

• Epidemic processes in complex networks
• Vital nodes identification in complex networks
• Identifying influential spreaders in complex networks based on gravity formula
• Identifying Influential Spreaders by Weighted LeaderRank
• Ranking in evolving complex networks
• A Survey on Centrality Metrics and Their Implications in Network Resilience
• Leveraging percolation theory to single out influential spreaders in networks
• Centrality measure based on continuous-time quantum walks and experimental realization
• Spatial super-spreaders and super-susceptibles in human movement networks
• Iterative resource allocation based on propagation feature of node for identifying the influential nodes
• Top influencers can be identified universally by combining classical centralities
• Influencers identification in complex networks through reaction-diffusion dynamics
• Quantum centrality testing on directed graphs via PT-symmetric quantum walks
• Centrality Measures in Complex Networks: A Survey
• Fast asynchronous updating algorithms for k-shell indices
• Rank the spreading influence of nodes using dynamic Markov process
• Weighted H-index for identifying influential spreaders
• Predicting Item Popularity: Analysing Local Clustering Behaviour of Users
• Finding the proper node ranking method for complex networks
• Promotion and resignation in employee networks
• Improving the accuracy of the k-shell method by removing redundant links-from a perspective of spreading dynamics