The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Structure and inference in annotated networks
Open accessAbstract
For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network. Here we demonstrate how this ‘metadata’ can be used to improve our understanding of network structure. We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone. Crucially, the method does not assume that the metadata are correlated with the communities we are trying to find. Instead, the method learns whether a correlation exists and correctly uses or ignores the metadata depending on whether they contain useful information. We demonstrate our method on synthetic networks with known structure and on real-world networks, large and small, drawn from social, biological and technological domains.
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Cited by
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Network analysis of multivariate data in psychological science
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00055-w · Cited by 1,371
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From maps to multi-dimensional network mechanisms of mental disorders
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Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison
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Metadata
- Authors
- Newman, M. E. J., & Clauset, A.
- Venue
- Nature Communications
- Published
- 2016-06-16
- Volume
- 7
- Issue
- 1
- Pages
- 11863-11863
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W1930572491
- Cited by
- 379
- References
- 50
- FWCI
- 29.97
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
