The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Searching for superspreaders of information in real-world social media
Open accessAbstract
A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.
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Cited by
Rosenthal, S. B., Twomey, C. R., Hartnett, A. T., Wu, H. S., & Couzin, I. D. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1420068112 · Cited by 474
Identifying influential spreaders in complex networks based on gravity formula
Ma, L.-L., Ma, C., Zhang, H.-F., & Wang, B.-H. (2016) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2015.12.162 · Cited by 367
Spontaneous Collective Action: Peripheral Mobilization During the Arab Spring
STEINERT-THRELKELD, Z. C. (2017) · American Political Science Review · doi:10.1017/s0003055416000769 · Cited by 331
Identifying a set of influential spreaders in complex networks
Zhang, J.-X., Chen, D.-B., Dong, Q., & Zhao, Z.-D. (2016) · Scientific Reports · doi:10.1038/srep27823 · Cited by 320
Data based identification and prediction of nonlinear and complex dynamical systems
Wang, W.-X., Lai, Y.-C., & Grebogi, C. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.06.004 · Cited by 299
Metadata
- Authors
- Pei, S., Muchnik, L., Andrade, Zheng, Z., & Makse, H. A.
- Venue
- Scientific Reports
- Published
- 2014-07-03
- Volume
- 4
- Issue
- 1
- Pages
- 5547-5547
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2166458546
- Cited by
- 362
- References
- 12
- FWCI
- 22.41
- Type
- journal article
- Language
- en
- Open access
- gold
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