The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Overlapping community detection at scale: a nonnegative matrix factorization approach
Abstract
Network communities represent basic structures for understanding the organization of real-world networks. A community (also referred to as a module or a cluster) is typically thought of as a group of nodes with more connections amongst its members than between its members and the remainder of the network. Communities in networks also overlap as nodes belong to multiple clusters at once. Due to the difficulties in evaluating the detected communities and the lack of scalable algorithms, the task of overlapping community detection in large networks largely remains an open problem.
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
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
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
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
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
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 13,868
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
A tutorial on spectral clustering
von Luxburg, U. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,349
Uncovering the overlapping community structure of complex networks in nature and society
Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005) · Nature · doi:10.1038/nature03607 · Cited by 5,373
Cited by
Community detection in networks: a user guide
Fortunato, S., & Hric, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.09.002 · Cited by 2,086
SNAP: A General-Purpose Network Analysis and Graph-Mining Library
Leskovec, J., & Sosič, R. (2017) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2898361 · Cited by 870
Self-citationCommunity detection in networks: A multidisciplinary review
Javed, M. A., Younis, M. S., Latif, S., Qadir, J., & Baig, A. (2018) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2018.02.011 · Cited by 499
MGAE: Marginalized Graph Autoencoder for Graph Clustering
Wang, C., Pan, S., Long, G., Zhu, X., & Jiang, J. (2017) · Proceedings of the 2017 ACM on Conference on Information and Knowledge Management · doi:10.1145/3132847.3132967 · Cited by 420
Xu, J., Mao, C., Hou, Y., Luo, Y., Binder, J. L., Zhou, Y., Bekris, L. M., Shin, J., Hu, M., Wang, F., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111717 · Cited by 115
Metadata
- Authors
- Yang, J., & Leskovec, J.
- Venue
- Proceedings of the sixth ACM international conference on Web search and data mining
- Publisher
- ACM
- Published
- 2013-02-04
- Pages
- 587-596
- OpenAlex
- W2139694940
- Cited by
- 852
- References
- 36
- FWCI
- 58.62
- 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 ›
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: Overlapping community detection at scale: a nonnegative matrix factorization approach. https://app.cochat.ai/papers/10.1145/2433396.2433471
- The paper
- Yang, J., & Leskovec, J. (2013). Overlapping community detection at scale: a nonnegative matrix factorization approach. Proceedings of the sixth ACM international conference on Web search and data mining. https://doi.org/10.1145/2433396.2433471
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.
