Efficient behavior of small-world networks
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We introduce the concept of efficiency of a network as a measure of how efficiently it exchanges information. By using this simple measure, small-world networks are seen as systems that are both globally and locally efficient. This gives a clear physical meaning to the concept of "small world," and also a precise quantitative analysis of both weighted and unweighted networks. We study neural networks and man-made communication and transportation systems and we show that the underlying general principle of their construction is in fact a small-world principle of high efficiency.
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Global efficiency is the inverse of path length but has a greater relative contribution from short-distance connections.
“A related measure is global efficiency, which is the inverse of path length, 60 but which has a greater relative contribution from short-distance connections. 1”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [60]
Local efficiency predominantly reflects short-range connections between neighboring brain regions that facilitate localized information processing.
“local efficiency predominantly reflects short-range connections between neighboring brain regions that facilitate localized information-processing”
From Developmental Changes in Organization of Structural Brain Networks (2013) · cites this paper as Latora and Marchiori 2001
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A Monte Carlo evaluation of weighted community detection algorithms
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“Network theory has been used to examine the organization of networks across disparate research foci, including the World Wide Web (Latora and Marchiori, 2001), social networks (Zachary, 1977), the power grid (Watts and Strogatz, 1998), brain processes (Bassett and Bullmore, 2006), and food-webs…”
MethodUrban Street Networks, a Comparative Analysis of Ten European Cities
Strano, E., Viana, M., da Fontoura Costa, L., Cardillo, A., Porta, S., & Latora, V. (2013) · Environment and Planning B: Planning and Design · doi:10.1068/b38216 · Cited by 121 · see citing works
Self-citationNetwork inefficiencies in autism spectrum disorder at 24 months.
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Multiple weak hits confuse complex systems: A transcriptional regulatory network as an example
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Changes in cognitive state alter human functional brain networks
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Dynamics of the human structural connectome underlying working memory training
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Myung, W., Han, C. E., Fava, M., Mischoulon, D., Papakostas, G. I., Heo, J.-Y., Kim, K. W., Kim, S. T., Kim, D. J. H., Kim, D. K., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.110 · Cited by 101 · see citing works
Modular gateway-ness connectivity and structural core organization in maritime network science
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Disrupted brain connectome in semantic variant of primary progressive aphasia
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Principles of recovery from traumatic brain injury: Reorganization of functional networks
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Network analysis reveals disrupted functional brain circuitry in drug-naive social anxiety disorder
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Functional properties of resting state networks in healthy full-term newborns
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Graph theoretical analysis of developmental patterns of the white matter network
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Altered brain network modules induce helplessness in major depressive disorder
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Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis
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Altered topology of the functional speech production network in non-fluent/agrammatic variant of PPA
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Stability of graph theoretical measures in structural brain networks in Alzheimer’s disease
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Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks
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Shared Leadership and Team Creativity: A Social Network Analysis in Engineering Design Teams
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Cluster-based statistics for brain connectivity in correlation with behavioral measures.
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- Authors
- Latora, V., & Marchiori, M.
- Affiliations
- Université Paris-Sud (FR)
- ORCID
- Latora, Vito
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2001-10-17
- Volume
- 87
- Issue
- 19
- Pages
- 198701-198701
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2060464767
- Cited by
- 5,119
- References
- 21
- Citations by year
- 2026: 306 · 2025: 342 · 2024: 332 · 2023: 341 · 2022: 347 · 2021: 388 · 2020: 373 · 2019: 287 · 2018: 331 · 2017: 246 · 2016: 263 · 2015: 230 · 2014: 203 · 2013: 246 · 2012: 153
- FWCI
- 10.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Technische Universität Dortmund Eldorado (Technische Universität Dortmund) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Technische Universität Dortmund Eldorado (Technische Universität Dortmund)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Mathematics › Geometry and Topology › Graph theory and applications
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- small-world networks, network efficiency, information sharing, weighted networks, unweighted networks, artificial neural networks, communication networks, transportation networks, network topology
- MeSH terms
- Brain, Models, Chemical, Models, Neurological, Models, Theoretical, Social Support, Transportation
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.
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