Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 5,406

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Fast algorithm for detecting community structure in networks

Open access
Newman, M. E. J.·2004·Physical Review E

Abstract

Many networks display community structure--groups of vertices within which connections are dense but between which they are sparser--and sensitive computer algorithms have in recent years been developed for detecting this structure. These algorithms, however, are computationally demanding, which limits their application to small networks. Here we describe an algorithm which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms. We give several example applications, including one to a collaboration network of more than 50,000 physicists.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1103/physreve.69.066133

Cites

3 of 3 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 3 references

Research works printed but not linked: 15

  1. 10.1038/35065725 · DOI · Paper page
  2. 10.1103/RevModPhys.74.47 · DOI · Paper page
  3. 10.1103/RevModPhys.74.47 · DOI · Paper page
  4. 10.1073/pnas.122653799 · DOI · Paper page
  5. 10.1073/pnas.0307740100 · DOI · Paper page
  6. 10.1093/bioinformatics/btg033 · DOI · Paper page
  7. 10.1103/PhysRevE.68.065103 · DOI · Paper page
  8. J. R. Tyler, 2003, Proceedings of the First International Conference on Communities and Technologies
  9. P. Gleiser, 2003, Adv. Complex Syst., 6, 565, 10.1142/S0219525903001067 · DOI · Paper page
  10. 10.1007/s100510050359 · DOI · Paper page
  11. 10.1073/pnas.021544898 · doi:10.1073/pnas.021544898 · Paper page
  12. 10.1126/science.1067014 · DOI · Paper page
  13. B. Everitt, 1974, Cluster Analysis
  14. J. Scott, 2000, Social Network Analysis: A Handbook, 10.1177/0038038588022001007 · DOI · Paper page
  15. W. W. Zachary, 1977, J. Anthropol. Res., 33, 452, 10.1086/jar.33.4.3629752 · DOI · Paper page

Cited by

See all 5,406

Metadata

Authors
Newman, M. E. J.
Venue
Physical Review E
Published
2004-06-18
Volume
69
Issue
6
Pages
066133-066133
ISSN
  • 1539-3755 (ISSN-L)
  • 1550-2376
OpenAlex
W2089458547
Cited by
5,406
References
3
FWCI
52.31
Type
journal article
Language
en
Open access
green

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Fast algorithm for detecting community structure in networks. https://app.cochat.ai/papers/10.1103/physreve.69.066133
The paper
Newman, M. E. J. (2004). Fast algorithm for detecting community structure in networks. Physical Review E. https://doi.org/10.1103/physreve.69.066133

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.