The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Community detection in networks: Structural communities versus ground truth
Open accessAbstract
Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (nontopological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.
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Cites
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Self-citationUncovering 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
The emergent properties of a dolphin social network
Lusseau, D. (2003) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rsbl.2003.0057 · Cited by 844
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
Self-citationA Comparative Analysis of Community Detection Algorithms on Artificial Networks
Yang, Z., Algesheimer, R., & Tessone, C. J. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-127494 · Cited by 685
A Comparative Analysis of Community Detection Algorithms on Artificial Networks
Yang, Z., Algesheimer, R., & Tessone, C. J. (2016) · Scientific Reports · doi:10.1038/srep30750 · Cited by 578
Structure and inference in annotated networks
Newman, M. E. J., & Clauset, A. (2016) · Nature Communications · doi:10.1038/ncomms11863 · Cited by 379
Klavans, R., & Boyack, K. W. (2017) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23734 · Cited by 334
Metadata
- Authors
- Hric, D., Darst, R. K., & Fortunato, S.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2014-12-09
- Volume
- 90
- Issue
- 6
- Pages
- 062805-062805
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W2090372231
- Cited by
- 238
- References
- 3
- FWCI
- 17.36
- Type
- journal article
- Language
- en
- Open access
- green
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- Hric, D., Darst, R. K., & Fortunato, S. (2014). Community detection in networks: Structural communities versus ground truth. Physical Review E. https://doi.org/10.1103/physreve.90.062805
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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