The DOI matches its Crossref record; 49 of the 50 references it cites were found in a registry or an index.
Measurement and analysis of online social networks
Abstract
Online social networking sites like Orkut, YouTube, and Flickr are among the most popular sites on the Internet. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks.
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“This rate is 23-fold lower than typical human social networks (20-30% reciprocity) 35,39 .”
From The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies (2026) · cites this paper as [39]
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Granovetter, M. S. (1973) · American Journal of Sociology · doi:10.1086/225469 · Cited by 40,491
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Social Network Analysis: Methods and Applications
Wasserman, S. S., & Faust, K. (1994) · Cited by 18,005
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502
(1999) · Cited by 12,854
Authoritative sources in a hyperlinked environment
Kleinberg, J. M. (1999) · Journal of the ACM · doi:10.1145/324133.324140 · Cited by 9,032
Power-law distributions in empirical data
Clauset, A. J., Shalizi, C. R., & Newman, M. E. J. (2018) · Figshare · doi:10.1184/r1/6586835.v1 · Cited by 6,710
Milgram, S. (1967) · PsycEXTRA Dataset · doi:10.1037/e400002009-005 · Cited by 5,528
Research works printed but not linked: 6
- 10.5555/646632.699477 · DOI
- A. Broder , R. Kumar , F. Maghoul , P. Raghavan , S. Rajagopalan , R. Stata , A. Tomkins , and J. Wiener . Graph Structure in the Web: Experiments and Models . In Proceedings of the 9th International World Wide Web Conference (WWW'00) , Amsterdam , May 2000 . A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. Wiener. Graph Structure in the Web: Experiments and Models. In Proceedings of the 9th International World Wide Web Conference (WWW'00), Amsterdam, May 2000.
- P. Erdös and A. Rényi . On Random Graphs I. Publicationes Mathematicae Debrecen , 5 : 290 -- 297 , 1959 . P. Erdös and A. Rényi. On Random Graphs I. Publicationes Mathematicae Debrecen, 5:290--297, 1959.
- 10.5555/874062.875507 · DOI
- MySpace is the number one website in the U. S. according to Hitwise . HitWise Press Release , July, 11, 2006 . http://www.hitwise.com/press-center/hitwiseHS2004/social-networking-june-2006.php. MySpace is the number one website in the U.S. according to Hitwise. HitWise Press Release, July, 11, 2006. http://www.hitwise.com/press-center/hitwiseHS2004/social-networking-june-2006.php.
- D. Rezner . The Power and Politics of Weblogs. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW'04) , Chicago, IL , Nov 2004 . D. Rezner. The Power and Politics of Weblogs. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW'04), Chicago, IL, Nov 2004.
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Analysis and mining of online social networks: emerging trends and challenges
Bhat, S. Y., & Abulaish, M. (2013) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1105 · Cited by 28
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Karimi, F., Ramenzoni, V. C., & Holme, P. (2014) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2014.07.037 · Cited by 27
Metadata
- Authors
- Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B.
- Affiliations
- Max Planck Institute for Software Systems (DE)
- University of Maryland, College Park (US)
- Venue
- Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
- Publisher
- ACM
- Published
- 2007-10-24
- Pages
- 29-42
- OpenAlex
- W2115022330
- Cited by
- 3,057
- References
- 50
- Citations by year
- 2026: 31 · 2025: 41 · 2024: 55 · 2023: 73 · 2022: 97 · 2021: 124 · 2020: 144 · 2019: 146 · 2018: 187 · 2017: 194 · 2016: 205 · 2015: 256 · 2014: 268 · 2013: 306 · 2012: 315
- FWCI
- 147.48
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Internet Measurement Conference (IMC) (publishedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- European Commission
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Keywords
- social networking sites, social graphs, large-scale graph analysis, Orkut, YouTube, Flickr, network topology
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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