The DOI matches its Crossref record.
Entropy rate of diffusion processes on complex networks
Open accessAbstract
We introduce the concept of entropy rate to characterize a diffusion process on a complex network. The entropy rate represents the minimal amount of information necessary to describe the diffusion on the network, and is a quantity extremely sensitive to the network topology and dynamics. By opportunely tuning the kind of diffusion, the entropy rate allows one to extract different properties of the network structure. Moreover, entropy maximization indicates how to design optimal diffusion processes, providing a new theoretical tool with applications to social, technological, and communication systems.
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
The structure and dynamics of multilayer networks
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014) · Physics Reports · doi:10.1016/j.physrep.2014.07.001 · Cited by 3,125
Self-citationRandom walks and diffusion on networks
Masuda, N., Porter, M. A., & Lambiotte, R. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.07.007 · Cited by 657
Entropy measures for networks: Toward an information theory of complex topologies
Anand, K., & Bianconi, G. (2009) · Physical Review E · doi:10.1103/physreve.80.045102 · Cited by 368
Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome
Teschendorff, A. E., & Enver, T. (2017) · Nature Communications · doi:10.1038/ncomms15599 · Cited by 346
Exploring the Morphospace of Communication Efficiency in Complex Networks
Goñi, J., Avena-Koenigsberger, A., Velez de Mendizabal, N., van den Heuvel, M. P., Betzel, R. F., & Sporns, O. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058070 · Cited by 158
Metadata
- Authors
- Gómez-Gardeñes, J., & Latora, V.
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 2008-12-11
- Volume
- 78
- Issue
- 6
- Pages
- 065102-065102
- ISSN
- 1539-3755 (ISSN-L)
- 1550-2376
- OpenAlex
- W1977280998
- Cited by
- 199
- References
- 0
- FWCI
- 6.02
- Type
- journal article
- Language
- en
- Open access
- green
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: Entropy rate of diffusion processes on complex networks. https://app.cochat.ai/papers/10.1103/physreve.78.065102
- The paper
- Gómez-Gardeñes, J., & Latora, V. (2008). Entropy rate of diffusion processes on complex networks. Physical Review E. https://doi.org/10.1103/physreve.78.065102
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.