The DOI matches its Crossref record.
Economic small-world behavior in weighted networks
Open accessAbstract
Abstract:The small-world phenomenon has been already the subject of a huge variety of papers, showing its appeareance in a variety of systems. However, some big holes still remain to be filled, as the commonly adopted mathematical formulation is valid only for topological networks. In this paper we propose a generalization of the theory of small worlds based on two leading concepts, efficiency and cost, and valid also for weighted networks. Efficiency measures how well information propagates over the network, and cost measures how expensive it is to build a network. The combination of these factors leads us to introduce the concept of economic small worlds, that formalizes the idea of networks that are “cheap” to build, and nevertheless efficient in propagating information, both at global and local scale. In this way we provide an adequate tool to quantitatively analyze the behaviour of complex networks in the real world. Various complex systems are studied, ranging from the realm of neural networks, to social sciences, to communication and transportation networks. In each case, economic small worlds are found. Moreover, using the economic small-world framework, the construction principles of these networks can be quantitatively analyzed and compared, giving good insights on how efficiency and economy principles combine up to shape all these 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 Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
Self-citationThe 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
Bassett, D. S., & Bullmore, E. (2006) · The Neuroscientist · doi:10.1177/1073858406293182 · Cited by 2,600
Efficiency and Cost of Economical Brain Functional Networks
Achard, S., & Bullmore, E. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030017 · Cited by 2,553
Barthélemy, M. (2011) · Physics Reports · doi:10.1016/j.physrep.2010.11.002 · Cited by 2,469
Structural Properties of the Caenorhabditis elegans Neuronal Network
Varshney, L. R., Chen, B. L., Paniagua, E., Hall, D. H., & Chklovskii, D. B. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001066 · Cited by 867
The network analysis of urban streets: A dual approach
Porta, S., Crucitti, P., & Latora, V. (2006) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2005.12.063 · Cited by 749
Self-citationSchizophrenia, neuroimaging and connectomics
Fornito, A., Zalesky, A., Pantelis, C., & Bullmore, E. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.090 · Cited by 716
Cortical high-density counterstream architectures
Markov, N. T., Ercsey-Ravasz, M., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013) · Science · doi:10.1126/science.1238406 · Cited by 616
Graph-based network analysis of resting-state functional MRI
Wang, J. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00016 · Cited by 614
A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule
Ercsey-Ravasz, M., Markov, N. T., Lamy, C., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.036 · Cited by 487
Topological organization of the human brain functional connectome across the lifespan
Cao, M., Wang, J.-H., Dai, Z.-J., Cao, X.-Y., Jiang, L.-L., Fan, F.-M., Song, X.-W., Xia, M.-R., Shu, N., Dong, Q., et al. (2014) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2013.11.004 · Cited by 440
Disrupted Functional Brain Connectome in Individuals at Risk for Alzheimer's Disease
Wang, J., Zuo, X., Dai, Z., Xia, M., Zhao, Z., Zhao, X., Jia, J., Han, Y., & He, Y. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.03.026 · Cited by 432
Cognitive Network Neuroscience
Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00810 · Cited by 419
Structural properties of planar graphs of urban street patterns
Cardillo, A., Scellato, S., Latora, V., & Porta, S. (2006) · Physical Review E · doi:10.1103/physreve.73.066107 · Cited by 370
Self-citationHallquist, M. N., Wright, A. G. C., & Molenaar, P. C. M. (2021) · Multivariate Behavioral Research · doi:10.1080/00273171.2019.1640103 · Cited by 292
Modelling the air transport with complex networks: A short review
Zanin, M., & Lillo, F. (2013) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2013-01711-9 · Cited by 277
Brain anatomical networks in early human brain development
Fan, Y., Shi, F., Smith, J. K., Lin, W., Gilmore, J. H., & Shen, D. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.025 · Cited by 225
Disrupted Small-World Brain Networks in Moderate Alzheimer's Disease: A Resting-State fMRI Study
Zhao, X., Liu, Y., Wang, X., Liu, B., Xi, Q., Guo, Q., Jiang, H., Jiang, T., & Wang, P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033540 · Cited by 215
Development Trends of White Matter Connectivity in the First Years of Life
Yap, P.-T., Fan, Y., Chen, Y., Gilmore, J. H., Lin, W., & Shen, D. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024678 · Cited by 190
Disruption of cerebral networks and cognitive impairment in Alzheimer disease.
Reijmer, Y. D., Leemans, A., Caeyenberghs, K., Heringa, S. M., Koek, H. L., Biessels, G. J., & On behalf of the Utrecht Vascular Cognitive Impairment Study Group (2013) · Neurology · doi:10.1212/wnl.0b013e31828c2ee5 · Cited by 147
The brain matures with stronger functional connectivity and decreased randomness of its network
Smit, D. J. A., Boersma, M., Schnack, H. G., Micheloyannis, S., Boomsma, D. I., Hulshoff Pol, H. E., Stam, C. J., & de Geus, E. J. C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036896 · Cited by 143
Network inefficiencies in autism spectrum disorder at 24 months.
Lewis, J. D., Evans, A. C., Pruett, J. R., Botteron, K., Zwaigenbaum, L., Estes, A., Gerig, G., Collins, L., Kostopoulos, P., McKinstry, R., et al. (2014) · Translational Psychiatry · doi:10.1038/tp.2014.24 · Cited by 110
Ginestet, C. E., & Simmons, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.030 · Cited by 112
Modular gateway-ness connectivity and structural core organization in maritime network science
Xu, M., Pan, Q., Muscoloni, A., Xia, H., & Cannistraci, C. V. (2020) · Nature Communications · doi:10.1038/s41467-020-16619-5 · Cited by 103
Functional properties of resting state networks in healthy full-term newborns
De Asis-Cruz, J., Bouyssi-Kobar, M., Evangelou, I., Vezina, G., & Limperopoulos, C. (2015) · Scientific Reports · doi:10.1038/srep17755 · Cited by 85
Graph theoretical analysis of developmental patterns of the white matter network
Chen, Z., Liu, M., Gross, D. W., & Beaulieu, C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00716 · Cited by 77
Atomoxetine and citalopram alter brain network organization in Parkinson’s disease
Borchert, R. J., Rittman, T., Rae, C. L., Passamonti, L., Jones, S. P., Vatansever, D., Vázquez Rodríguez, P., Ye, Z., Nombela, C., Hughes, L. E., et al. (2019) · Brain Communications · doi:10.1093/braincomms/fcz013 · Cited by 14
Metadata
- Authors
- Latora, V., & Marchiori, M.
- Venue
- The European Physical Journal B - Condensed Matter and Complex Systems
- Published
- 2003-03
- Volume
- 32
- Issue
- 2
- Pages
- 249-263
- ISSN
- 1434-6028 (ISSN-L)
- 1434-6036
- OpenAlex
- W2006052322
- Cited by
- 772
- References
- 0
- FWCI
- 10.84
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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
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.