The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Revealing Network Connectivity from Response Dynamics
Open accessAbstract
We present a method to infer the complete connectivity of a network from its stable response dynamics. As a paradigmatic example, we consider networks of coupled phase oscillators and explicitly study their long-term stationary response to temporally constant driving. For a given driving condition, measuring the phase differences and the collective frequency reveals information about how the units are interconnected. Sufficiently many repetitions for different driving conditions yield the entire network connectivity (the absence or presence of each connection) from measuring the response dynamics only. For sparsely connected networks, we obtain good predictions of the actual connectivity even for formally underdetermined problems.
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Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Network motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling
Gardner, T. S., di Bernardo, D., Lorenz, D., & Collins, J. J. (2003) · Science · doi:10.1126/science.1081900 · Cited by 1,117
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Metadata
- Authors
- Timme, M.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2007-05-30
- Volume
- 98
- Issue
- 22
- Pages
- 224101-224101
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2022704310
- Cited by
- 333
- References
- 3
- FWCI
- 16.27
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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.
