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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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.
Cited by
Sorted by citations
Items 201–300 of 1,001
Understanding Graph Sampling Algorithms for Social Network Analysis
Wang, T., Chen, Y., Zhang, Z., Xu, T., Jin, L., Hui, P., Deng, B., & Li, X. (2011) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw.2011.34 · Cited by 105
Analysis of Online Social Network Connections for Identification of Influential Users
Al-Garadi, M. A., Varathan, K. D., Ravana, S. D., Ahmed, E., Mujtaba, G., Khan, M. U. S., & Khan, S. U. (2018) · ACM Computing Surveys · doi:10.1145/3155897 · Cited by 107
Pavan, E. (2016) · Social movement studies · doi:10.1080/14742837.2016.1268956 · Cited by 106
What Trends in Chinese Social MediaPreprint
Yu, L. L., Asur, S., & Huberman, B. A. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1107.3522 · Cited by 101
Estimating clustering coefficients and size of social networks via random walk
Hardiman, S. J., & Katzir, L. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488436 · Cited by 100
Measuring user behavior in online social networks
Gyarmati, L., & Trinh, T. A. (2010) · IEEE Network · doi:10.1109/mnet.2010.5578915 · Cited by 100
de‐Marcos, L., García‐López, E., García‐Cabot, A., Medina, J., Domínguez‐Díaz, A., Martínez-Herráiz, J.-J., & Díez-Folledo, T. I. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.02.052 · Cited by 102
Direction-Optimizing Breadth-First Search
Beamer, S., Asanović, K., & Patterson, D. A. (2013) · Scientific Programming · doi:10.1155/2013/702694 · Cited by 100
Anonymizing weighted social network graphs
Das, S., Eğecioğlu, Ö., & Abbadi, A. E. (2010) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2010.5447915 · Cited by 101
Lofgren, P., Banerjee, S., Goel, A., & Seshadhri, C. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623745 · Cited by 101
Measurements, Analyses, and Insights on the Entire Ethereum Blockchain Network
Lee, X. T., Khan, A., Gupta, S. S., Ong, Y. H., & Liu, X. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380103 · Cited by 98
Abbas, Z., Kalavri, V., Carbone, P., & Vlassov, V. (2018) · Proceedings of the VLDB Endowment · doi:10.14778/3236187.3236208 · Cited by 97
Gjoka, M., Sirivianos, M., Markopoulou, A. P., & Yang, X. (2008) · doi:10.1145/1397735.1397743 · Cited by 97
A general framework for geo-social query processing
Armenatzoglou, N., Papadopoulos, S., & Dimitris Papadias, C. (2013) · Proceedings of the VLDB Endowment · doi:10.14778/2536206.2536218 · Cited by 96
From Technological Networks to Social Networks
Chen, K., Chiang, M., & Poor, H. V. (2013) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2013.sup.0513049 · Cited by 95
Wattenhofer, M., Wattenhofer, R. P., & Zhu, Z. Z. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14243 · Cited by 96
Scalable similarity search for SimRank
Kusumoto, M., Maehara, T., & Kawarabayashi, K. (2014) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2588555.2610526 · Cited by 95
The Role of Online Social Network Travel Websites in Creating Social Interaction for Gen Y Travelers
Nusair, K., Bilgihan, A., & Okumuş, F. (2012) · International Journal of Tourism Research · doi:10.1002/jtr.1889 · Cited by 96
Siemens, R., Timney, M., Leitch, C., Koolen, C., & Garnett, A. (2012) · Literary and Linguistic Computing · doi:10.1093/llc/fqs013 · Cited by 95
Opinion leader detection using whale optimization algorithm in online social network
Jain, L., Katarya, R., & Sachdeva, S. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.113016 · Cited by 95
On random walk based graph sampling
Li, R.-H., Yu, J. X., Qin, L., Mao, R., & Jin, T. (2015) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2015.7113345 · Cited by 95
What Trends in Chinese Social MediaPreprint
Asur, S., Yu, L. L., & Huberman, B. A. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1888779 · Cited by 95
Managing large graphs on multi-cores with graph awareness
Prabhakaran, V., Wu, M., Weng, X., McSherry, F., Zhou, L., & Haridasan, M. (2012) · USENIX Annual Technical Conference · Cited by 94
Networks of Ethereum Non-Fungible Tokens: A graph-based analysis of the ERC-721 ecosystem
Casale-Brunet, S., Ribeca, P., Doyle, P., & Mattavelli, M. (2021) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain53845.2021.00033 · Cited by 94
A tale of the tails: Power-laws in internet measurements
Mahanti, A., Carlsson, N. F., Mahanti, A., Arlitt, M. F., & Williamson, C. L. (2013) · IEEE Network · doi:10.1109/mnet.2013.6423193 · Cited by 94
A Trust-Based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks
Guo, L., Zhang, C., & Fang, Y. M. (2014) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2014.2355824 · Cited by 93
Comparing Twitter and Facebook user behavior: Privacy and other aspects
Buccafurri, F., Lax, G., Nicolazzo, S., & Nocera, A. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.05.045 · Cited by 92
Efficiently Estimating Motif Statistics of Large Networks
Wang, P., Lui, J. C. S., Ribeiro, B., Towsley, D., Zhao, J., & Guan, X. (2014) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2629564 · Cited by 91
Information resonance on Twitter
Zhou, Z., Bandari, R., Kong, J. S., Qian, H., & Roychowdhury, V. (2010) · doi:10.1145/1964858.1964875 · Cited by 90
Computationally efficient link prediction in a variety of social networks
Fire, M., Chekina, L., Puzis, R., Lesser, O., Rokach, L., & Elovici, Y. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2542182.2542192 · Cited by 90
Ding, Y., Du, Y., Hu, Y., Liu, Z., Wang, L., Ross, K. W., & Ghose, A. (2011) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2068816.2068850 · Cited by 90
Alleviating Irregularity in Graph Analytics Acceleration
Yan, M., Hu, X., Li, S., Basak, A., Li, H., Ma, X., Akgun, I., Feng, Y., Gu, P., Deng, L., et al. (2019) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3352460.3358318 · Cited by 90
An epidemic model of rumor diffusion in online social networks
Cheng, J.-J., Liu, Y., Shen, B., & Yuan, W.-G. (2013) · The European Physical Journal B · doi:10.1140/epjb/e2012-30483-5 · Cited by 90
Chang, Y., Tang, L., Inagaki, Y., & Liu, Y. (2014) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2674026.2674030 · Cited by 89
Propagation-based social-aware replication for social video contents
Wang, Z., Sun, L., Chen, X., Zhu, W., Liu, J., Chen, M., & Yang, S. (2012) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2393347.2393359 · Cited by 89
Measuring and modeling bipartite graphs with community structure
Aksoy, S. G., Kolda, T. G., & Pinar, A. (2017) · Journal of Complex Networks · doi:10.1093/comnet/cnx001 · Cited by 93
Information propagation in online social networks: a tie-strength perspective
Zhao, J., Wu, J., Xu, F., Xiong, H., & Xu, K. (2011) · Knowledge and Information Systems · doi:10.1007/s10115-011-0445-x · Cited by 89
Motoyama, M., & Varghese, G. (2009) · ACM International Workshop on Web Information and Data Management (WIDM) · doi:10.1145/1651587.1651604 · Cited by 89
Disassortative mixing in online social networks
Hu, H.-B., & Wang, X.-F. (2009) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/86/18003 · Cited by 89
Akhtar, N. (2014) · International Conference on Communication Systems and Network Technologies · doi:10.1109/csnt.2014.83 · Cited by 89
Dynamics of Public Opinions in an Online and Offline Social Network
Dong, Y., Ding, Z., Chiclana, F., & Herrera‐Viedma, E. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2676810 · Cited by 90
Ohme, J. (2018) · Information Communication & Society · doi:10.1080/1369118x.2018.1469657 · Cited by 104
Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks
Breuer, A., Eilat, R., & Weinsberg, U. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380204 · Cited by 88
True scale-free networks hidden by finite size effects
Serafino, M., Cimini, G., Maritan, A., Rinaldo, A., Suweis, S. S., Banavar, J. R., & Caldarelli, G. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2013825118 · Cited by 103
Computing classic closeness centrality, at scale
Cohen, E., Delling, D., Pajor, T., & Werneck, R. F. (2014) · Conference on Online Social Networks · doi:10.1145/2660460.2660465 · Cited by 88
DynaMo: Dynamic Community Detection by Incrementally Maximizing Modularity
Zhuang, D., Chang, J. M., & Li, M. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2951419 · Cited by 92
Are we ready for metaverse?: a measurement study of social virtual reality platforms
Cheng, R.-Z., Wu, N., Varvello, M., Chen, S.-Q., & Han, B. (2022) · ACM/SIGCOMM Internet Measurement Conference · doi:10.1145/3517745.3561417 · Cited by 114
Exploring the cloud from passive measurements: The Amazon AWS case
Bermudez, I. N., Traverso, S., Mellia, M., & Munafò, M. M. (2013) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2013.6566769 · Cited by 87
Choi, J., & Lee, J. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.11.029 · Cited by 90
Solutions to Security and Privacy Issues in Mobile Social Networking
Beach, A., Gartrell, M., & Han, R. Y. (2009) · doi:10.1109/cse.2009.243 · Cited by 87
A proposed social network analysis platform for big data analytics
Chang, V. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.11.002 · Cited by 86
Information Diffusion on Social Media During Natural Disasters
Dong, R., Li, L., Zhang, Q., & Cai, G. (2018) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2017.2786545 · Cited by 85
Sampling directed graphs with random walks
Ribeiro, B., Wang, P., Murai, F., & Towsley, D. (2012) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2012.6195540 · Cited by 86
Identifying Influential Nodes in Online Social Networks Using Principal Component Centrality
Ilyas, M., & Radha, H. (2011) · International Conference on Communications · doi:10.1109/icc.2011.5963147 · Cited by 87
Sabet, A. H. N., Zhao, Z., & Gupta, R. (2020) · European Conference on Computer Systems · doi:10.1145/3342195.3387537 · Cited by 85
Toward worm detection in online social networks
Xu, W., Zhang, F., & Zhu, S. (2010) · Annual Computer Security Applications Conference · doi:10.1145/1920261.1920264 · Cited by 83
A Walk in Facebook: Uniform Sampling of Users in Online Social NetworksPreprint
Gjoka, M., Kurant, M., Butts, C. T., & Markopoulou, A. P. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0906.0060 · Cited by 83
Magno, G., Comarela, G., Sáez-Trumper, D., Cha, M., & Almeida, V. A. F. (2012) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2398776.2398794 · Cited by 82
Video interactions in online video social networks
Benevenuto, F., Rodrigues, T., Almeida, V. A. F., Almeida, J. M., & Ross, K. W. (2009) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/1596990.1596994 · Cited by 84
Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs
Tretyakov, K., Armas-Cervantes, A., García‐Bañuelos, L., Vilo, J., & Dumas, M. (2011) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2063576.2063834 · Cited by 82
Cost-Effective Viral Marketing for Time-Critical Campaigns in Large-Scale Social Networks
Dinh, T. N., Zhang, H., Nguyen, D. T., & Thai, M. T. (2014) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2013.2290714 · Cited by 81
Sabet, A. H. N., Qiu, J., & Zhao, Z. (2018) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3173162.3173180 · Cited by 81
Towards understanding cyberbullying behavior in a semi-anonymous social network
Hosseinmardi, H., Ghasemian, A., Han, R. Y., Lv, Q., & Mishra, S. (2014) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2014.6921591 · Cited by 81
Understanding video interactions in youtube
Benevenuto, F., Duarte, F., Rodrigues, T., Almeida, V. A. F., Almeida, J. M., & Ross, K. W. (2008) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1459359.1459480 · Cited by 81
Understanding music sharing behaviour on social network services
Lee, D., Park, J. Y., Kim, J., Kim, J., & Moon, J. (2011) · Online Information Review · doi:10.1108/14684521111176462 · Cited by 81
A routing protocol for socially selfish delay tolerant networks
Li, Q., Gao, W., Zhu, S., & Cao, G. (2011) · Ad Hoc Networks · doi:10.1016/j.adhoc.2011.07.007 · Cited by 80
Trust Transitivity in Complex Social Networks
Liu, G., Wang, Y., & Orgun, M. (2011) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v25i1.8087 · Cited by 87
Evolution of a location-based online social network
Allamanis, M., Scellato, S., & Cecilia Mascolo, C. (2012) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2398776.2398793 · Cited by 79
TriCore: Parallel Triangle Counting on GPUs
Hu, Y., Liu, H., & Huang, H. H. (2018) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc.2018.00017 · Cited by 79
Urban and social sensing for sustainable mobility in smart cities
Anastasi, G., Antonelli, M., Bechini, A., Brienza, S., D’Andrea, E., Guglielmo, D. D., Ducange, P., Lazzerini, B., Marcelloni, F., & Segatori, A. (2013) · Sustainable Internet and ICT for Sustainability (SustainIT) · doi:10.1109/sustainit.2013.6685198 · Cited by 78
Network Community Detection: A Review and Visual SurveyPreprint
Khan, B. S., & Niazi, M. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.00977 · Cited by 78
Global RDF Vector Space Embeddings
Cochez, M., Ristoski, P., Ponzetto, S. P., & Paulheim, H. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68288-4_12 · Cited by 80
Positive Influence Dominating Set in Online Social Networks
Wang, F., Camacho, E. T., & Xu, K. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02026-1_29 · Cited by 77
DOGMA: A Disk-Oriented Graph Matching Algorithm for RDF Databases
Broecheler, M., Pugliese, A., & Subrahmanian, V. S. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04930-9_7 · Cited by 77
Vo, T. C., Sharma, R., Kumar, R., Sơn, L. H., Pham, B. T., Bui, D. T., Priyadarshini, I., Sarkar, M., & Le, T. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-190870 · Cited by 76
A Concept of Location-Based Social Network Marketing
Tussyadiah, I. P. (2012) · Journal of Travel & Tourism Marketing · doi:10.1080/10548408.2012.666168 · Cited by 76
Efficient discovery of influential nodes for SIS models in social networks
Saito, K., Kimura, M., Ohara, K., & Motoda, H. (2011) · Knowledge and Information Systems · doi:10.1007/s10115-011-0396-2 · Cited by 76
Organization Mining Using Online Social Networks
Fire, M., & Puzis, R. (2015) · Networks and Spatial Economics · doi:10.1007/s11067-015-9288-4 · Cited by 75
Should We Use the Sample? Analyzing Datasets Sampled from Twitter’s Stream API
Wang, Y., Callan, J., & Zheng, B. (2015) · ACM Transactions on the Web · doi:10.1145/2746366 · Cited by 76
Mining social networks for anomalies: Methods and challenges
Bindu, P. V., & Thilagam, P. S. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.02.021 · Cited by 80
Evaluation of Advertising Campaigns on Social Media Networks
Raudeliūnienė, J., Davidavičienė, V., Tvaronavičienė, M., & Jonuška, L. (2018) · Sustainability · doi:10.3390/su10040973 · Cited by 76
Discovering community patterns in open-source: a systematic approach and its evaluation
Tamburri, D. A., Palomba, F., Serebrenik, A., & Zaidman, A. (2018) · Empirical Software Engineering · doi:10.1007/s10664-018-9659-9 · Cited by 76
Privometer: Privacy protection in social networks
Talukder, N., Ouzzani, M., Elmagarmid, A. K., Elmeleegy, H., & Yakout, M. (2010) · IEEE International Conference on Data Engineering Workshops (ICDEW) · doi:10.1109/icdew.2010.5452715 · Cited by 75
A Graph-Based Socioeconomic Analysis of Steemit
Guidi, B., Michienzi, A., & Ricci, L. (2020) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.3042745 · Cited by 75
Bodaghi, A., & Oliveira, J. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116110 · Cited by 78
Wedge sampling for computing clustering coefficients and triangle counts on large graphs †
Seshadhri, C., Pinar, A., & Kolda, T. G. (2014) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11224 · Cited by 75
Maximizing acceptance probability for active friending in online social networks
Yang, D.-N., Hung, H.-J., Lee, W.-C., & Chen, W. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487599 · Cited by 74
Online social networks in economics
Mayer, A. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.02.009 · Cited by 74
Krishnan, D. R., Quoc, D. L., Bhatotia, P., Fetzer, C. W., & Rodrigues, R. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883026 · Cited by 70
Are You a Cyborg, Bot or Human?—A Survey on Detecting Fake News Spreaders
Shahid, W., Li, Y., Staples, D., Gilkar, G. A., Hakak, S., & Ghorbani, A. A. (2022) · IEEE Access · doi:10.1109/access.2022.3157724 · Cited by 79
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey
Ekle, O. A., & Eberle, W. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3669906 · Cited by 93
Bressan, M., Chierichetti, F., Kumar, R., Leucci, S., & Panconesi, A. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018732 · Cited by 73
Sun, J., Vandierendonck, H., & Nikolopoulos, D. S. (2017) · International Conference on Supercomputing · doi:10.1145/3079079.3079097 · Cited by 73
Alike people, alike interests? Inferring interest similarity in online social networks
Han, X. G., Wang, L., Crespi, N., Park, S., & Cuevas, √. (2014) · Decision Support Systems · doi:10.1016/j.dss.2014.11.008 · Cited by 72
ReaderBench: Automated evaluation of collaboration based on cohesion and dialogism
Dascălu, M., Trăușan-Matu, Ș., McNamara, D. S., & Dessus, P. (2015) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-015-9226-y · Cited by 77
Personality traits, usage patterns and information disclosure in online communities
Schrammel, J., Köffel, C., & Tscheligi, M. (2009) · doi:10.5555/1671011.1671031 · Cited by 72
Link Creation and Profile Alignment in the aNobii Social Network
Aiello, L. M., Barrat, A., Cattuto, C., Ruffo, G., & Schifanella, R. (2010) · doi:10.1109/socialcom.2010.42 · Cited by 71
A Cryptoeconomic Traffic Analysis of Bitcoin’s Lightning Network
Béres, F., Seres, I. A., & Benczúr, A. A. (2020) · doi:10.21428/58320208.d4cd697e · Cited by 71
Attea, B. A., Abbood, A. D., Hasan, A. A., Pizzuti, C., Al-Ani, M. F., Özdemi̇r, S., & Al-Dabbagh, R. D. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100885 · Cited by 72
Temporal Analysis of the Entire Ethereum Blockchain Network
Zhao, L., Gupta, S. S., Khan, A., & Luo, R. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449916 · Cited by 71
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review