The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Identifying influential spreaders in complex networks based on gravity formula
Open accessAbstract
How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gravity centrality index to identify the influential spreaders in complex networks. The comparison between the gravity centrality index and some well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, and k-shell centrality, and so forth, indicates that our method can effectively identify the influential spreaders in real networks as well as synthetic networks. We also use the classical Susceptible–Infected–Recovered (SIR) epidemic model to verify the good performance of our method.
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Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
A Set of Measures of Centrality Based on Betweenness
Freeman, L. C. (1977) · Sociometry · doi:10.2307/3033543 · Cited by 10,295
Finding community structure in networks using the eigenvectors of matrices
Newman, M. E. J. (2006) · Physical Review E · doi:10.1103/physreve.74.036104 · Cited by 4,761
Catastrophic cascade of failures in interdependent networks
Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E., & Havlin, S. (2010) · Nature · doi:10.1038/nature08932 · Cited by 4,513
Identification of influential spreaders in complex networks
Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010) · Nature Physics · doi:10.1038/nphys1746 · Cited by 3,212
Factoring and weighting approaches to status scores and clique identification
Bonacich, P. (1972) · The Journal of Mathematical Sociology · doi:10.1080/0022250x.1972.9989806 · Cited by 3,207
Specificity and Stability in Topology of Protein Networks
Maslov, S., & Sneppen, K. (2002) · Science · doi:10.1126/science.1065103 · Cited by 3,051
The Centrality Index of a Graph
Sabidussi, G. (1966) · Psychometrika · doi:10.1007/bf02289527 · Cited by 2,476
Graph evolution: Densification and shrinking diameters
Leskovec, J., Kleinberg, J., & Faloutsos, C. (2007) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1217299.1217301 · Cited by 2,461
Metadata
- Authors
- Ma, L.-L., Ma, C., Zhang, H.-F., & Wang, B.-H.
- Venue
- Physica A: Statistical Mechanics and its Applications
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 451
- Pages
- 205-212
- ISSN
- 0378-4371 (ISSN-L)
- OpenAlex
- W2161953836
- Cited by
- 367
- References
- 49
- FWCI
- 11.72
- Type
- journal article
- Language
- en
- Open access
- green
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- Ma, L.-L., Ma, C., Zhang, H.-F., & Wang, B.-H. (2016). Identifying influential spreaders in complex networks based on gravity formula. Physica A: Statistical Mechanics and its Applications. https://doi.org/10.1016/j.physa.2015.12.162
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