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Phys Rev E. 2017 Aug;96(2-1):022323. doi: 10.1103/PhysRevE.96.022323. Epub 2017 Aug 31.

Accurate ranking of influential spreaders in networks based on dynamically asymmetric link weights.

Liu Y1,2, Tang M1,3, Do Y4, Hui PM5.

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Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China.
School of Computer Science, Southwest Petroleum University, Chengdu 610500, China.
School of Information Science Technology, East China Normal University, Shanghai 200241, China.
Department of Mathematics, Kyungpook National University, Daegu 702-701, South Korea.
Department of Physics, Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.


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 w_{ij} and w_{ji} 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 s_{i}, which quantifies the impact of the node in spreading processes. An s-shell decomposition scheme further assigns an s-shell index or weighted coreness to the nodes. The effectiveness and accuracy of rankings based on s_{i} 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.

[Indexed for MEDLINE]

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