Betweenness centrality in large complex networks
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Highly connected nodes (hubs) have been identified through a variety of means that quantify how central these nodes are in the network.
“Highly connected nodes have been identified through a variety of means 7,40 that quantify how central these nodes are in the network.96”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [7]
Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135 · see citing works
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557 · see citing works
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Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820 · see citing works
Synchronization in complex networks
Arenas, A., Díaz-Guilera, A., Kurths, J., Moreno, Y., & Zhou, C. (2008) · Physics Reports · doi:10.1016/j.physrep.2008.09.002 · Cited by 3,713 · see citing works
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 · see citing works
Dichter, G. S., Gibbs, D., & Smoski, M. J. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.09.028 · Cited by 375 · see citing works
Deep Recursive Network Embedding with Regular Equivalence
Tu, K., Cui, P., Wang, X., Yu, P. S., & Zhu, W. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220068 · Cited by 179 · see citing works
Hart, M. G., Ypma, R. J. F., Romero-Garcia, R., Price, S. J., & Suckling, J. (2016) · Journal of Neurosurgery · doi:10.3171/2015.4.jns142683 · Cited by 74 · see citing works
- Authors
- Barthélemy, M.
- Affiliations
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Corresponding author
- Barthélemy, M.
- ORCID
- Barthélemy, M.
- Venue
- The European Physical Journal B
- Published
- 2004-03
- Volume
- 38
- Issue
- 2
- Pages
- 163-168
- ISSN
- 1434-6028 (ISSN-L)
- 1434-6036
- OpenAlex
- W2152255412
- Cited by
- 804
- References
- 2
- Citations by year
- 2026: 39 · 2025: 38 · 2024: 65 · 2023: 53 · 2022: 50 · 2021: 49 · 2020: 57 · 2019: 40 · 2018: 45 · 2017: 47 · 2016: 52 · 2015: 38 · 2014: 44 · 2013: 32 · 2012: 18
- FWCI
- 15.23
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- The European Physical Journal B (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Springer Link (Chiba Institute of Technology) (submittedVersion)
- HAL (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
- Keywords
- betweenness centrality, complex networks, scale-free networks, power-law distribution, loop density, trees, connectivity distribution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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