Identifying influential spreaders by gravity model
Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and pa...
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| Published in | Scientific reports Vol. 9; no. 1; p. 8387 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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London
Nature Publishing Group UK
10.06.2019
Nature Publishing Group |
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| Abstract | Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node’s importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network. |
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| AbstractList | Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node’s importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network. Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node's importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network.Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. Inspired by the gravity law, we propose a gravity model that utilizes both neighborhood information and path information to measure a node's importance in spreading dynamics. In order to reduce the accumulated errors caused by interactions at distance and to lower the computational complexity, a local version of the gravity model is further proposed by introducing a truncation radius. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on fourteen real networks show that the gravity model and the local gravity model perform very competitively in comparison with well-known state-of-the-art methods. For the local gravity model, the empirical results suggest an approximately linear relation between the optimal truncation radius and the average distance of the network. |
| ArticleNumber | 8387 |
| Author | Zhang, Yixin Li, Zhe Ren, Tao Zhou, Tao Ma, Xiaoqi Liu, Simiao |
| Author_xml | – sequence: 1 givenname: Zhe surname: Li fullname: Li, Zhe organization: Software College, Northeastern University of China – sequence: 2 givenname: Tao surname: Ren fullname: Ren, Tao email: chinarentao@163.com organization: Software College, Northeastern University of China – sequence: 3 givenname: Xiaoqi orcidid: 0000-0003-0074-4192 surname: Ma fullname: Ma, Xiaoqi organization: School of Science and Technology, Nottingham Trent University – sequence: 4 givenname: Simiao surname: Liu fullname: Liu, Simiao organization: Software College, Northeastern University of China – sequence: 5 givenname: Yixin surname: Zhang fullname: Zhang, Yixin organization: Software College, Northeastern University of China – sequence: 6 givenname: Tao surname: Zhou fullname: Zhou, Tao email: zhutou@ustc.edu organization: CompleX Lab, University of Electronic Science and Technology of China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31182773$$D View this record in MEDLINE/PubMed |
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