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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Reciprocity is 23-fold lower than typical human social networks (20-30%).
“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]
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Detecting spammers and content promoters in online video social networks
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Impact of social network structure on content propagation: A study using YouTube data
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Zhao, L., Wang, Q., Cheng, J., Chen, Y., Wang, J., & Huang, W. (2011) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2011.03.010 · Cited by 219 · see citing works
Crawling Facebook for social network analysis purposes
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A Survey of Caching Policies and Forwarding Mechanisms in Information-Centric Networking
Ioannou, A., & Weber, S. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2565541 · Cited by 218 · see citing works
Heat kernel based community detection
Kloster, K., & Gleich, D. F. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623706 · Cited by 215 · see citing works
Distance matters: geo-social metrics for online social networks
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Social-Network-Sourced Big Data Analytics
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Social Network Structure of a Large Online Community for Smoking Cessation
Cobb, N. K., Graham, A. L., & Abrams, D. B. (2010) · American Journal of Public Health · doi:10.2105/ajph.2009.165449 · Cited by 210 · see citing works
Ahmed, N. K., Neville, J., & Kompella, R. R. (2013) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2601438 · Cited by 241 · see citing works
The Privacy Jungle:On the Market for Data Protection in Social Networks
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Zhao, P., Han, J., & Sun, Y. (2009) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1645953.1646025 · Cited by 207 · see citing works
Query preserving graph compression
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Understanding latent interactions in online social networks
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A First Step Towards Understanding Popularity in YouTube
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Identifying user behavior in online social networks
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Commenting on YouTube videos: From guatemalan rock to El Big Bang
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Comparison of online social relations in volume vs interaction: A case study of Cyworld
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Understanding Graph-Based Trust Evaluation in Online Social Networks
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CoFIM: A community-based framework for influence maximization on large-scale networks
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Measuring the mixing time of social graphs
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Online social networking: redefining the human web
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The life and death of online groups
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Link Prediction in Social Networks Using Computationally Efficient Topological Features
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Secure friend discovery in mobile social networks
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Breakdown of interdependent directed networks
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- 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 › Computer Vision and Pattern Recognition › 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.
What counts as a claim ›How we measure confidence ›How we check this ›
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