Accurate ranking of influential spreaders in networks based on dynamically asymmetric link-impact

Abstract

We propose an efficient and accurate measure for ranking spreaders and identifying the influential ones in spreading processes in networks. While the edges determine the connections among the nodes, their specific role in spreading should be considered explicitly. An edge connecting nodes i and j may differ in its importance for spreading from i to j and from j to i. The key issue is whether node j, after infected by i through the edge, would reach out to other nodes that i itself could not reach directly. It becomes necessary to invoke two unequal weights wij and wji characterizing the importance of an edge according to the neighborhoods of nodes i and j. The total asymmetric directional weights originating from a node leads to a novel measure si which quantifies the impact of the node in spreading processes. A s-shell decomposition scheme further assigns a s-shell index or weighted coreness to the nodes. The effectiveness and accuracy of rankings based on si and the weighted coreness are demonstrated by applying them to nine real-world networks. Results show that they generally outperform rankings based on the nodes' degree and k-shell index, while maintaining a low computational complexity. Our work represents a crucial step towards understanding and controlling the spread of diseases, rumors, information, trends, and innovations in networks.

9 pages, 8 figures

References in corpus (28)

• An index to quantify an individual's scientific research output
• Finding community structure in networks using the eigenvectors of matrices
• The structure of scientific collaboration networks
• The architecture of complex weighted networks
• Random graphs with arbitrary degree distributions and their applications
• Identification of influential spreaders in complex networks
• Critical phenomena in complex networks
• Attack vulnerability of complex networks
• Influence maximization in complex networks through optimal percolation
• Vital nodes identification in complex networks
• Unification of theoretical approaches for epidemic spreading on complex networks
• Localization and centrality in networks
• Graph Evolution: Densification and Shrinking Diameters
• A k-shell decomposition method for weighted networks
• Spreading dynamics in complex networks
• General Dynamics of Topology and Traffic on Weighted Technological Networks
• Understanding the spreading power of all nodes in a network: a continuous-time perspective
• Percolation and Epidemic Thresholds in Clustered Networks
• Robust classification of salient links in complex networks
• Identify influential spreaders in complex networks, the role of neighborhood
• Clustering in complex networks. I. General formalism
• Competing activation mechanisms in epidemics on networks
• Identifying influential spreaders and efficiently estimating infection numbers in epidemic models: a walk counting approach
• Onion structure and network robustness
• Leveraging percolation theory to single out influential spreaders in networks
• Efficient routing strategies in scale-free networks with limited bandwidth
• Beyond the locally tree-like approximation for percolation on real networks
• Impact of edge-removal on the centrality betweenness of the best spreaders

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