The DOI matches its Crossref record.
Communities of Interest
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
Cited by
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
Statistical Fraud Detection: A Review
Bolton, R. J., & Hand, D. J. (2002) · Statistical Science · doi:10.1214/ss/1042727940 · Cited by 1,450
Data Streams: Algorithms and Applications
Muthukrishnan, S. (2005) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000002 · Cited by 718
Classification in Networked Data: A Toolkit and a Univariate Case Study
(2007) · Journal of Machine Learning Research · doi:10.5555/1248659.1248693 · Cited by 452
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage
Hooi, B., Song, H. A., Beutel, A., Shah, N., Shin, K., & Faloutsos, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939747 · Cited by 305
Metadata
- Authors
- Cortes, C., Pregibon, D., & Volinsky, C.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2001
- Pages
- 105-114
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W1933130724
- Cited by
- 192
- References
- 0
- FWCI
- 4.29
- Type
- conference paper
- 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 ›
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Communities of Interest. https://app.cochat.ai/papers/10.1007/3-540-44816-0_11
- The paper
- Cortes, C., Pregibon, D., & Volinsky, C. (2001). Communities of Interest. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-44816-0_11
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.
