The DOI matches its Crossref record.
Evolution of Networks
Abstract
Only recently did mankind realise that it resides in a world of networks. The Internet and World Wide Web are changing our life. Our physical existence is based on various biological networks. We have recently learned that the term network turns out to be a central notion in our time, and the consequent explosion of interest in networks is a social and cultural phenomenon. The principles of the complex organization and evolution of networks, natural and artificial, are the topic of this book, which is written by physicists and is addressed to all involved researchers and students. The aim of the text is to understand networks and the basic principles of their structural organization and evolution. The ideas are presented in a clear and a pedagogical way, with minimal mathematics, so even students without a deep knowledge of mathematics and statistical physics will be able to rely on this as a reference. Special attention is given to real networks, both natural and artifical. Collected empirical data and numerous real applications of existing theories are discussed in detail, as well as the topical problems of communication networks. Available in OSO: http://www.oxfordscholarship.com/oso/public/content/physics/9780198515906/toc.html
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Using the master equation approach [50-52] we show that depending on the dimensions d and δ , and on the flavor s , the generalized degrees k d,δ can follow either binomial or exponential or power-law distributions.”
From Network geometry with flavor: From complexity to quantum geometry (2016) · cites this paper as [50]
In the citing paper
“For deriving our results at β = 0 we use the master equation approach [50-52] that provides exact asymptotic solutions for the distribution.”
From Network geometry with flavor: From complexity to quantum geometry (2016) · cites this paper as [50]
In the citing paper
“Using the master equation approach [50-52] , it is possible to derive the exact distribution for the generalized degrees. We indicate with N t, [1] d,δ ( k ) the average number of δ -faces that at time t have generalized degree k d,δ = k . The master equation[50-52] for N t, [1] d,δ ( k ) reads”
From Network geometry with flavor: From complexity to quantum geometry (2016) · cites this paper as [50]
In the citing paper
“In NGF with flavor s = 1 and β = 0 each ( d -1) -face α ∈ S d,d -1 has a probability to be selected proportional to its generalized degree k d,d -1 ( α ). In fact the probability Π [1] α defined in Eq. ( S -5) includes an explicit preferential attachment mechanism [50-52, 60] as is given by”
From Network geometry with flavor: From complexity to quantum geometry (2016) · cites this paper as [50]
In the citing paper
“Strong clustering is another structural property often observed in complex networks: average clustering in random graphs of similar size and average degree is lower by orders of magnitude8910.”
From Network Cosmology (2012) · cites this paper as [8]
In the citing paper
“In many (but not all) complex networks the degree distribution is also a power law with γ close to 28910.”
From Network Cosmology (2012) · cites this paper as [8]
Citations
Cited by
Sorted by citations
Items 301–400 of 1,007
Identifying video spammers in online social networks
Benevenuto, F., Rodrigues, T., Almeida, V. A. F., et al. (2008) · doi:10.1145/1451983.1451996 · Cited by 100
Newman, M. E. J. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780198805090.003.0006 · Cited by 108
Hypergraph topological quantities for tagged social networks
Zlatić, V., Ghoshal, G., & Caldarelli, G. (2009) · Physical Review E · doi:10.1103/physreve.80.036118 · Cited by 105
Ventresca, M. (2012) · Computers & Operations Research · doi:10.1016/j.cor.2012.02.008 · Cited by 98
Mapping stochastic processes onto complex networks
Shirazi, A. H. M., Jafari, G., Davoudi, J., et al. (2009) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2009/07/p07046 · Cited by 98
Categorical structure among shared features in networks of early-learned nouns
Hills, T. T., Maouene, M., Maouene, J., Sheya, A., & Smith, L. B. (2009) · Cognition · doi:10.1016/j.cognition.2009.06.002 · Cited by 97
From the time series to the complex networks: The parametric natural visibility graph
Bezsudnov, I. V., & Snarskii, A. A. (2014) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2014.07.002 · Cited by 98
Gibbs entropy of network ensembles by cavity methods
Anand, K., & Bianconi, G. (2010) · Physical Review E · doi:10.1103/physreve.82.011116 · Cited by 95
A review on models and algorithms for motif discovery in protein-protein interaction networks
Ciriello, G., & Guerra, C. (2008) · Briefings in Functional Genomics and Proteomics · doi:10.1093/bfgp/eln015 · Cited by 99
Jenkins, S., & Kirk, S. R. (2007) · Information Sciences · doi:10.1016/j.ins.2007.01.021 · Cited by 98
Hein, O., Schwind, M. A., & König, W. (2006) · WIRTSCHAFTSINFORMATIK · doi:10.1007/s11576-006-0058-2 · Cited by 98
A comparative study of network robustness measures
Liu, J., Zhou, M., Wang, S., & Liu, P. (2017) · Frontiers of Computer Science · doi:10.1007/s11704-016-6108-z · Cited by 95
Analyzing the Structure and Evolution of Massive Telecom Graphs
Nanavati, A. A., Singh, R. K., Chakraborty, D., Dasgupta, K., Mukherjea, S., Das, G., Gurumurthy, S., & Joshi, A. (2008) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.190733 · Cited by 96
Network transitivity and matrix models
Burda, Z., Jurkiewicz, J., & Krzywicki, A. (2004) · Physical Review E · doi:10.1103/physreve.69.026106 · Cited by 94
Connecting the Dots: Anatomical Network Analysis in Morphological EvoDevo
Rasskin‐Gutman, D., & Esteve‐Altava, B. (2014) · Biological Theory · doi:10.1007/s13752-014-0175-x · Cited by 97
Network analyses in microbiome based on high-throughput multi-omics data
Liu, Z., Ma, A., Mathé, E. A., Merling, M. R., Ma, Q., & Liu, B. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa005 · Cited by 93
Random networks with tunable degree distribution and clustering
Volz, E. (2004) · Physical Review E · doi:10.1103/physreve.70.056115 · Cited by 91
Lessons from Three Views of the Internet TopologyPreprint
Mahadevan, P., Krioukov, D., Fomenkov, M., et al. (2005) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0508033 · Cited by 94
The Chip Is the Network: Toward a Science of Network-on-Chip Design
Mărculescu, R., & Bogdan, P. (2009) · Foundations and Trends® in Electronic Design Automation · doi:10.1561/1000000011 · Cited by 93
Weighted growing simplicial complexes
Courtney, O. T., & Ginestra Bianconi, A. (2017) · Physical review. E · doi:10.1103/physreve.95.062301 · Cited by 85
The complexity of Chinese syntactic dependency networks
Liu, H. (2008) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2008.01.069 · Cited by 92
Random walks on deterministic scale-free networks: Exact results
Agliari, E., & Burioni, R. (2009) · Physical Review E · doi:10.1103/physreve.80.031125 · Cited by 92
A global view of drug-therapy interactions
Nacher, J. C., & Schwartz, J. (2008) · BMC Pharmacology · doi:10.1186/1471-2210-8-5 · Cited by 94
Dynamics of N-person snowdrift games in structured populations
Santos, M. D., Pinheiro, F. L., Santos, F. C., & Pacheco, J. M. (2012) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2012.09.001 · Cited by 87
Travel and tourism: Into a complex network
Miguéns, J., & Mendes, J. F. F. (2008) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2008.01.058 · Cited by 91
Self-citationPercolation of interdependent network of networks
Havlin, S., Stanley, E., Bashan, A., Gao, J., & Kenett, D. Y. (2014) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2014.09.006 · Cited by 89
Percolation in hierarchical scale-free nets
Rozenfeld, H. D., & ben‐Avraham, D. (2007) · Physical Review E · doi:10.1103/physreve.75.061102 · Cited by 90
Multifractal network generator
Palla, G., Lovász, L., & Vicsek, T. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912983107 · Cited by 84
Kitsak, M., Havlin, S., Paul, G., et al. (2007) · Physical Review E · doi:10.1103/physreve.75.056115 · Cited by 89
Atay, Y., Koç, İ., Babaoğlu, İ., et al. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.11.025 · Cited by 89
Statistical physics of language dynamics
Loreto, V., Baronchelli, A., Mukherjee, A., Andrea Puglisi, B., & Tria, F. (2011) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2011/04/p04006 · Cited by 86
An adaptive routing strategy for packet delivery in complex networks
Zhang, H., Liu, Z., Tang, M., & Hui, P. M. (2006) · Physics Letters A · doi:10.1016/j.physleta.2006.12.009 · Cited by 89
The scaling laws of transport properties for fractal-like tree networks
Xu, P., & Yu, B. (2006) · Journal of Applied Physics · doi:10.1063/1.2392935 · Cited by 90
Robustness of a partially interdependent network formed of clustered networks
Shao, S., Huang, X., Stanley, H. E., et al. (2014) · Physical Review E · doi:10.1103/physreve.89.032812 · Cited by 88
Asymmetric disassembly and robustness in declining networks
Saavedra, S., Reed‐Tsochas, F., & Uzzi, B. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0804740105 · Cited by 87
Evolution of Controllability in Interbank Networks
Delpini, D., Battiston, S., Riccaboni, M., Gabbi, G., Fabio Pammolli, A., & Caldarelli, G. (2013) · Scientific Reports · doi:10.1038/srep01626 · Cited by 89
Protein networking: insights into global functional organization of proteomes
Pieroni, E., van Bentem, S. D. L. F., Mancosu, G., Capobianco, E., Hirt, H., & de la Fuente, A. (2008) · PROTEOMICS · doi:10.1002/pmic.200700767 · Cited by 86
Computing communities in large networks using random walksPreprint
Latapy, M., & Pons, P. (2004) · arXiv (Cornell University) · doi:10.48550/arxiv.cond-mat/0412368 · Cited by 87
Emergence of weight-topology correlations in complex scale-free networks
Bianconi, G. (2005) · Europhysics Letters (EPL) · doi:10.1209/epl/i2005-10167-2 · Cited by 85
Phase transitions for rock-scissors-paper game on different networks
Szolnoki, A., & Szabó, G. (2004) · Physical Review E · doi:10.1103/physreve.70.037102 · Cited by 86
Scale-free networks with an exponent less than two
Seyed-allaei, H., Ginestra Bianconi, A., & Matteo Marsili, A. (2006) · Physical Review E · doi:10.1103/physreve.73.046113 · Cited by 87
Characterization of weighted complex networks
Park, K., Lai, Y.-C., & Ye, N. (2004) · Physical Review E · doi:10.1103/physreve.70.026109 · Cited by 86
Balancing System Survivability and Cost of Smart Grid Via Modeling Cascading Failures
Huang, Z., Wang, C., Stojmenović, M., et al. (2013) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2013.2273079 · Cited by 85
Complex earthquake networks: Hierarchical organization and assortative mixing
Abe, S., & Suzuki, N. (2006) · Physical Review E · doi:10.1103/physreve.74.026113 · Cited by 79
Between “design” and “bricolage”: Genetic networks, levels of selection, and adaptive evolution
Wilkins, A. S. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701044104 · Cited by 82
Stability of Climate Networks with Time
Berezin, Y., Gozolchiani, A., Guez, O., & Havlin, S. (2012) · Scientific Reports · doi:10.1038/srep00666 · Cited by 78
Ising model in scale-free networks: A Monte Carlo simulation
Herrero, C. P. (2004) · Physical Review E · doi:10.1103/physreve.69.067109 · Cited by 81
Global similarity and local divergence in human and mouse gene co-expression networks
Tsaparas, P., Mariño‐Ramírez, L., Bodenreider, O., Koonin, E. V., & Jordan, I. K. (2006) · BMC Evolutionary Biology · doi:10.1186/1471-2148-6-70 · Cited by 79
Zhou, M., & Liu, J. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2520477 · Cited by 81
Nonlinear growth and condensation in multiplex networks
Nicosia, V., Ginestra Bianconi, A., Latora, V., & Barthélemy, M. (2014) · Physical Review E · doi:10.1103/physreve.90.042807 · Cited by 78
Non-Stationary Traffic Analysis and Its Implications on Multicore Platform Design
Bogdan, P., & Mărculescu, R. (2011) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2011.2111270 · Cited by 80
High-Dimensional Graphical Model Search with thegRapHDRPackage
de Abreu, G. C. G., Edwards, D., & Labouriau, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v037.i01 · Cited by 80
Structural Transitions in Densifying Networks
Lambiotte, R., Krapivsky, P. L., Bhat, U., & Redner, S. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.218301 · Cited by 79
(2016) · Routledge eBooks · doi:10.4324/9781315637228 · Cited by 81
Detecting overlapping communities by seed community in weighted complex networks
Li, J., Wang, X., & Eustace, J. (2013) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2013.07.066 · Cited by 80
Epidemic spreading with nonlinear infectivity in weighted scale-free networks
Chu, X., Zhang, Z., Guan, J., & Zhou, S. (2010) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.09.038 · Cited by 78
Shape classification using complex network and Multi-scale Fractal Dimension
Backes, A. R., & Bruno, O. M. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.08.007 · Cited by 80
Information Indices with High Discriminative Power for Graphs
Dehmer, M., Grabner, M., & Вармуза, К. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031214 · Cited by 78
Esteve‐Altava, B., Marugán‐Lobón, J., Botella, H., & Rasskin‐Gutman, D. (2012) · Evolutionary Biology · doi:10.1007/s11692-012-9200-9 · Cited by 79
Complex Networks Renormalization: Flows and Fixed Points
Radicchi, F., Ramasco, J. J., Barrat, A., & Fortunato, S. (2008) · Physical Review Letters · doi:10.1103/physrevlett.101.148701 · Cited by 77
Immunization of real complex communication networks
Gómez‐Gardeñes, J., Echenique, P. M., & Moreno, Y. (2006) · The European Physical Journal B · doi:10.1140/epjb/e2006-00041-1 · Cited by 78
OPTIMAL PATH AND MINIMAL SPANNING TREES IN RANDOM WEIGHTED NETWORKS
Braunstein, L. A., Wu, Z., Chen, Y., Buldyrev, S. V., Tomer Kalisky, C., Sreenivasan, S., Reuven Cohen, C., López, E., Havlin, S., & Stanley, E. (2007) · International Journal of Bifurcation and Chaos · doi:10.1142/s0218127407018361 · Cited by 74
An Analysis of the Japanese Credit Network
Masi, G., Fujiwara, Y., Gallegati, M., Greenwald, B. C. N., & Stiglitz, J. E. (2011) · Evolutionary and Institutional Economics Review · doi:10.14441/eier.7.209 · Cited by 76
Braha, D., Suh, N. P., Eppinger, S. D., Caramanis, M. C., & Frey, D. D. (2007) · Springer eBooks · doi:10.1007/978-3-540-35872-5_6 · Cited by 75
A study on coevolutionary dynamics of knowledge diffusion and social network structure
Luo, S., Du, Y., Liu, P., Xuan, Z., & Wang, Y. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.12.038 · Cited by 75
Yuan, X., Shao, S., Stanley, H. E., et al. (2015) · Physical Review E · doi:10.1103/physreve.92.032122 · Cited by 76
Baronchelli, A., Catanzaro, M., & Pastor‐Satorras, R. (2008) · Physical Review E · doi:10.1103/physreve.78.011114 · Cited by 76
The use of dynamical networks to detect the hierarchical organization of financial market sectors
Matteo, T. D., Pozzi, F., & Aste, T. (2009) · The European Physical Journal B · doi:10.1140/epjb/e2009-00286-0 · Cited by 78
Dynamics of hate based Internet user networks
Sobkowicz, P., & Sobkowicz, A. (2010) · The European Physical Journal B · doi:10.1140/epjb/e2010-00039-0 · Cited by 77
Hit Miss Networks with Applications to Instance Selection
Marchiori, E. (2008) · Cited by 75
An Empirical Analysis of the Network Structure of the Austrian Interbank Market 1
Boss, M., Elsinger, H., Summer, M., & Stefan Thurner, A. (2003) · RePEc: Research Papers in Economics · Cited by 69
Role of feedback and broadcasting in the naming game
Baronchelli, A. (2011) · Physical Review E · doi:10.1103/physreve.83.046103 · Cited by 74
Entropy of Dynamical Social Networks
Zhao, K., Karsai, M., & Bianconi, G. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028116 · Cited by 78
Emergence of Assortative Mixing between Clusters of Cultured Neurons
Teller, S., Granell, C., Domenico, M. D., Soriano, J., Gómez, S., & Arenas, À. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003796 · Cited by 73
Exploring the assortativity-clustering space of a network’s degree sequence
Holme, P., & Zhao, J. (2007) · Physical Review E · doi:10.1103/physreve.75.046111 · Cited by 73
Evolution of document networks
Menczer, F. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307554100 · Cited by 71
The network of concepts in written texts
Caldeira, S. M. G., Lobão, T. C. P., Andrade, R. F. S., et al. (2006) · The European Physical Journal B · doi:10.1140/epjb/e2006-00091-3 · Cited by 74
The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks
Scatà, M., Stefano, A. D., Lió, P., & Corte, A. L. (2016) · Scientific Reports · doi:10.1038/srep37105 · Cited by 72
A likelihood approach to analysis of network data
Wiuf, C., Brameier, M., Hagberg, O., & Stumpf, M. P. H. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600061103 · Cited by 73
GRAPH THEORY AND COMPLEX NETWORKS
Duan, Z. (2008) · Lixue jinzhan · Cited by 76
Decoding the structure of the WWW
Serrano, M. Á., Maguitman, A. G., Boguñá, M., Fortunato, S., & Vespignani, A. (2007) · ACM Transactions on the Web · doi:10.1145/1255438.1255442 · Cited by 75
Self-avoiding walks on scale-free networks
Herrero, C. P. (2005) · Physical Review E · doi:10.1103/physreve.71.016103 · Cited by 72
Adaptive Contact Networks Change Effective Disease Infectiousness and Dynamics
Segbroeck, S. V., Santos, F. C., & Pacheco, J. M. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000895 · Cited by 72
Social inertia in collaboration networks
Ramasco, J. J., & Morris, S. A. (2006) · Physical Review E · doi:10.1103/physreve.73.016122 · Cited by 72
Communities in Italian corporate networks
Piccardi, C., Calatroni, L., & Bertoni, F. (2010) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.06.038 · Cited by 69
Ranking nodes in growing networks: When PageRank fails
Mariani, M. S., Medo, M., & Zhang, Y. (2015) · Scientific Reports · doi:10.1038/srep16181 · Cited by 70
The organization of strong links in complex networks
Pajevic, S., & Plenz, D. (2012) · Nature Physics · doi:10.1038/nphys2257 · Cited by 71
Structural constraints in complex networks
Zhou, S., & Mondragón, R. J. (2007) · New Journal of Physics · doi:10.1088/1367-2630/9/6/173 · Cited by 70
k -core percolation on complex networks: Comparing random, localized, and targeted attacks
Yuan, X., Dai, Y., Stanley, H. E., et al. (2016) · Physical review. E · doi:10.1103/physreve.93.062302 · Cited by 71
The ontogeny of scale-free syntax networks: phase transitions in early language acquisition
COROMINAS-MURTRA, B., VALVERDE, S., & SOLÉ, R. (2009) · Advances in Complex Systems · doi:10.1142/s0219525909002192 · Cited by 71
Population Structure Induces a Symmetry Breaking Favoring the Emergence of Cooperation
Pacheco, J. M., Pinheiro, F. L., & Santos, F. C. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000596 · Cited by 67
Dynamic networks and directed percolation
Parshani, R., Dickison, M. E., Cohen, R., et al. (2010) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/90/38004 · Cited by 70
Preferential attachment of communities: the same principle, but a higher levelPreprint
Pollner, P., Palla, G., & Vicsek, T. (2006) · Cited by 72
Bibri, S. E. (2021) · Computational Urban Science · doi:10.1007/s43762-021-00001-2 · Cited by 70
Deciphering the global organization of clustering in real complex networks
Colomer-de-Simón, P., Serrano, M. Á., Beiró, M. G., Alvarez-Hamelin, J. I., & Boguñá, M. (2013) · Scientific Reports · doi:10.1038/srep02517 · Cited by 66
The structural role of weak and strong links in a financial market network
Garas, A., Argyrakis, P., & Havlin, S. (2008) · The European Physical Journal B · doi:10.1140/epjb/e2008-00237-3 · Cited by 69
Self-organized scale-free networks
Park, K., Lai, Y., & Ye, N. (2005) · Physical Review E · doi:10.1103/physreve.72.026131 · Cited by 69
Constructing and sampling directed graphs with given degree sequencesPreprint
Kim, H., del Genio, C. I., Bassler, K. E., Toroczkai, Z., Str, N., & Dresden, D. (2012) · Cited by 72
Viscoelastic Networks: Forming Cells and Tissues
Corominas-Murtra, B., & Petridou, N. I. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.666916 · Cited by 69
THE LARGEST COMPONENT IN A SUBCRITICAL RANDOM GRAPH WITH A POWER LAW DEGREE DISTRIBUTION
Janson, S. (2008) · Cited by 68
Metadata
- Authors
- Dorogovtsev, S. N., & Mendes, J. F. F.
- Venue
- Oxford University Press eBooks
- Publisher
- Oxford University Press
- Published
- 2003-01-16
- OpenAlex
- W4210901545
- Cited by
- 1,563
- References
- 0
- Citations by year
- 2026: 2 · 2025: 17 · 2024: 16 · 2023: 27 · 2022: 11 · 2021: 29 · 2020: 31 · 2019: 26 · 2018: 48 · 2017: 30 · 2016: 50 · 2015: 74 · 2014: 73 · 2013: 90 · 2012: 105
- FWCI
- 25.00
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Oxford University Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Mathematics › Discrete Mathematics and Combinatorics › Graph theory and applications
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- network evolution, network structure, complex networks, internet, World Wide Web, communication networks
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
