The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Self-organization and identification of Web communities
Abstract
The vast improvement in information access is not the only advantage resulting from the increasing percentage of hyperlinked human knowledge available on the Web. Additionally, much potential exists for analyzing interests and relationships within science and society. However, the Web's decentralized and unorganized nature hampers content analysis. Millions of individuals operating independently and having a variety of backgrounds, knowledge, goals and cultures author the information on the Web. Despite the Web's decentralized, unorganized, and heterogeneous nature, our work shows that the Web self-organizes and its link structure allows efficient identification of communities. This self-organization is significant because no central authority or process governs the formation and structure of hyperlinks.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Research works printed but not linked: 17
- 10.1177/016555158000200109 · DOI
- 10.1515/9781400841356.514 · DOI
- 10.1016/S1389-1286(99)00040-7 · DOI · Paper page
- 10.1145/238386.238450 · DOI · Paper page
- 10.1145/276627.276652 · DOI · Paper page
- 10.1016/S1389-1286(99)00052-3 · DOI · Paper page
- Chung, 1996, Spectral Graph Theory, CBMS Lecture Notes, 10.1090/cbms/092 · DOI
- 10.1145/290941.290972 · DOI · Paper page
- Garey, 1979, Computers and Intractability: A Guide to the Theory of NP-Completeness
- 10.1126/science.280.5360.95 · DOI · Paper page
- 10.1038/30918 · DOI · Paper page
- 10.4153/CJM-1956-045-5 · DOI
- 10.1145/48014.61051 · DOI · Paper page
- Ahuja, 1993, Network Flows: Theory, Algorithms, and Applications
- 10.1145/347090.347121 · DOI
- 10.1038/43601 · DOI · Paper page
- Novak, 1998, Bridging the Digital Divide: The Impact of Race on Computer Access and Internet Use, Science, 919
Cited by
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
Finding community structure in very large networks
Clauset, A., Newman, M. E. J., & Moore, C. (2004) · Physical Review E · doi:10.1103/physreve.70.066111 · Cited by 7,390
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224
Resolution limit in community detection
Fortunato, S., & Barthélemy, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0605965104 · Cited by 3,024
Defining and identifying communities in networks
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400054101 · Cited by 2,450
The Wealth of Networks: How Social Production Transforms Markets and Freedom
(2006) · Choice Reviews Online · doi:10.5860/choice.44-0999 · Cited by 1,903
Group formation in large social networks: membership, growth, and evolution
Backstrom, L., Huttenlocher, D., Kleinberg, J., & Lan, X. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150412 · Cited by 1,805
Communities, modules and large-scale structure in networks
Newman, M. E. J. (2012) · Nature Physics · doi:10.1038/nphys2162 · Cited by 787
Maximal planar networks with large clustering coefficient and power-law degree distribution
Zhou, T., Yan, G., & Wang, B.-H. (2005) · Physical Review E · doi:10.1103/physreve.71.046141 · Cited by 265
It's who you know: graph mining using recursive structural features
Henderson, K., Gallagher, B., Li, L., Akoglu, L., Eliassi-Rad, T., Tong, H., & Faloutsos, C. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020512 · Cited by 223
BETZEL, R. F., GRIFFA, A., AVENA-KOENIGSBERGER, A., GOÑI, J., THIRAN, J.-P., HAGMANN, P., & SPORNS, O. (2013) · Network Science · doi:10.1017/nws.2013.19 · Cited by 113
Mowshowitz, A., & Kawaguchi, A. (2005) · Information Processing & Management · doi:10.1016/j.ipm.2004.05.005 · Cited by 68
I/O-efficient techniques for computing pagerank
Chen, Y.-Y., Gan, Q., & Suel, T. (2002) · Proceedings of the eleventh international conference on Information and knowledge management · doi:10.1145/584792.584882 · Cited by 59
Community Detection in Quantum Complex Networks
Faccin, M., Migdał, P., Johnson, T. H., Bergholm, V., & Biamonte, J. D. (2014) · Physical Review X · doi:10.1103/physrevx.4.041012 · Cited by 43
Metadata
- Authors
- Flake, G. W., Lawrence, S., Giles, C. L., & Coetzee, F. M.
- Venue
- Computer
- Published
- 2002-03
- Volume
- 35
- Issue
- 3
- Pages
- 66-70
- ISSN
- 0018-9162 (ISSN-L)
- OpenAlex
- W2102931907
- Cited by
- 1,018
- References
- 2
- FWCI
- 30.09
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
