The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Phase transitions in random networks: Simple analytic determination of critical points
Abstract
The standard method for determining the critical points in random networks (such as Boolean nets) is based on the Derrida approximation. This method leads to the critical points based on the so-called annealed approximation. In this paper, we present a very simple annealed approximation based on the study of damage spreading when single elements are modified. Several examples are analyzed.
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
“Interestingly, the basic McCulloch–Pitts design is common to many other living systems, including gene regulatory networks [256–259], immune networks [260–262], collective intelligence [263–267] or some aneural systems, such as quorum-sensing decisions in microbial communities, where an explicit equivalence has been defined [268].”
From Fundamental constraints to the logic of living systems (2024) · cites this paper as [258]
Citations
Cites
The Origins of Order: Self-Organization and Selection in Evolution
Kauffman, S. A. (1993) · doi:10.1093/oso/9780195079517.001.0001 · Cited by 7,855
Metabolic stability and epigenesis in randomly constructed genetic nets
Kauffman, S. A. (1969) · Journal of Theoretical Biology · doi:10.1016/0022-5193(69)90015-0 · Cited by 4,871
Bak, P., Tang, C., & Wiesenfeld, K. A. (1988) · Physical Review A · doi:10.1103/physreva.38.364 · Cited by 4,744
Universality and complexity in cellular automata
Wolfram, S. (1984) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(84)90245-8 · Cited by 1,956
Random Networks of Automata: A Simple Annealed Approximation
Derrida, B., & Pomeau, Y. (1986) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/1/2/001 · Cited by 688
The Theory of Critical Phenomena: An Introduction to the Renormalization Group
Binney, J. J., Dowrick, N. J., Fisher, A., & Newman, M. E. J. (1992) · CERN Document Server (European Organization for Nuclear Research) · Cited by 515
Modeling the complexity of genetic networks: Understanding multigenic and pleiotropic regulation
Somogyi, R., & Sniegoski, C. A. (1996) · Complexity · doi:10.1002/cplx.6130010612 · Cited by 340
Emergent properties in random complex automata
Kauffman, S. A. (1984) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(84)90257-4 · Cited by 256
The large scale structure and dynamics of gene control circuits
Kauffman, S. (1974) · Journal of Theoretical Biology · doi:10.1016/s0022-5193(74)80037-8 · Cited by 232
Phase Transitions in Two-Dimensional Kauffman Cellular Automata
Derrida, B., & Stauffer, D. (1986) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/2/10/001 · Cited by 214
Research works printed but not linked: 1
- S. A. Kauffman, 1995, At Home in the Universe
Cited by
Sorted by citations
Non-equilibrium critical phenomena and phase transitions into absorbing states
Haye Hinrichsen, B. (2000) · Advances In Physics · doi:10.1080/00018730050198152 · Cited by 1,752
Boolean dynamics of networks with scale-free topology
Aldana, M. (2003) · Physica D Nonlinear Phenomena · doi:10.1016/s0167-2789(03)00174-x · Cited by 407
Boolean Dynamics with Random Couplings
Aldana, M., Coppersmith, S. N., & Kadanoff, L. P. (2003) · Springer eBooks · doi:10.1007/978-0-387-21789-5_2 · Cited by 311
Small-World Networks: Evidence for a Crossover Picture
Barthélemy, M., & Amaral, L. A. N. (1999) · Physical Review Letters · doi:10.1103/physrevlett.82.3180 · Cited by 307
Dynamics of Complex Systems: Scaling Laws for the Period of Boolean Networks
Albert, R., & Barabási, A.-Ĺ. (2000) · Physical Review Letters · doi:10.1103/physrevlett.84.5660 · Cited by 253
A natural class of robust networks
Aldana, M., & Cluzel, P. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1536783100 · Cited by 250
Introduction to Random Boolean Networks
Gershenson, C. (2004) · arXiv · doi:10.48550/arxiv.nlin/0408006 · Cited by 125
On the Characterization of the Structural Robustness of Data Center Networks
Bilal, K., Manzano, M., Khan, S. U., Calle, E., Li, K., & Zomaya, A. Y. (2013) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2013.6 · Cited by 113
The effect of network topology on the stability of discrete state models of genetic control
Pomerance, A., Ott, E., Girvan, M., & Losert, W. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0900142106 · Cited by 105
From topology to dynamics in biochemical networks
Fox, J. J., & Hill, C. C. (2001) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.1414882 · Cited by 102
Ordered and disordered dynamics in random networks
Glass, L., & Hill, C. C. (1998) · Europhysics Letters (EPL) · doi:10.1209/epl/i1998-00199-0 · Cited by 77
Guiding the self-organization of random Boolean networks
Gershenson, C. (2012) · Theory in Biosciences · doi:10.1007/s12064-011-0144-x · Cited by 68
Criticality in random threshold networks: annealed approximation and beyond
Rohlf, T., & Bornholdt, S. (2002) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(02)00798-7 · Cited by 66
Criticality in random threshold networks: annealed approximation and beyond
Rohlf, T., & Bornholdt, S. (2002) · RePEc: Research Papers in Economics · Cited by 56
Boolean Modelingof Genetic Regulatory Networks
Albert, R. (2004) · Lecture notes in physics · doi:10.1007/978-3-540-44485-5_21 · Cited by 54
Common Chaos in Arbitrarily Complex Feedback Networks
Mestl, T., Bagley, R. J., & Glass, L. (1997) · Physical Review Letters · doi:10.1103/physrevlett.79.653 · Cited by 48
Damage Spreading and Criticality in Finite Random Dynamical Networks
Rohlf, T., Gulbahce, N., & Teuscher, C. (2007) · Physical Review Letters · doi:10.1103/physrevlett.99.248701 · Cited by 48
Emergent criticality from coevolution in random Boolean networks
Liu, M., & Bassler, K. E. (2006) · Physical Review E · doi:10.1103/physreve.74.041910 · Cited by 47
Boolean dynamics of Kauffman models with a scale-free network
Iguchi, K., Kinoshita, S.-I., & Yamada, H. S. (2007) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2007.02.010 · Cited by 48
Chaotic gene regulatory networks can be robust against mutations and noise
Sevi̇m, V., & Rikvold, P. A. (2008) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2008.03.003 · Cited by 45
T-matrix theory of positron annihilation on hydrogen
Ryzhikh, G., & Mitroy, J. (2000) · Journal of Physics B Atomic Molecular and Optical Physics · doi:10.1088/0953-4075/33/12/306 · Cited by 44
Dynamics in high-dimensional model gene networks
Kappler, K. N., Edwards, R., & Glass, L. (2003) · Signal Processing · doi:10.1016/s0165-1684(02)00479-6 · Cited by 40
The phase diagram of random threshold networks
Szejka, A., Mihaljev, T., & Drossel, B. (2024) · Technischen Universität Darmstadt · doi:10.26083/tuprints-00020547 · Cited by 38
The Role of Computation in Complex Regulatory Networks
Fernández, P., & Solé, R. V. (2007) · Springer eBooks · doi:10.1007/0-387-33916-7_12 · Cited by 38
Self-citationFundamental constraints to the logic of living systems
Solé, R., Kempes, C. P., Corominas-Murtra, B., De Domenico, M., Kolchinsky, A., Lachmann, M., Libby, E., Saavedra, S., Smith, E., & Wolpert, D. (2024) · Interface Focus · doi:10.1098/rsfs.2024.0010 · Cited by 34
“It is easy to show that three fixed points exist, associated with a symmetry-breaking (pitchfork) bifurcation [366]. t T A potential function (as defined in one-dimensional dynamical systems, see [258]) can be derived from d /d = − d ( )/d , i.e. from”
BackgroundSelf-citationThe phase diagram of random threshold networks
Szejka, A., Mihaljev, T., & Drossel, B. (2008) · New Journal of Physics · doi:10.1088/1367-2630/10/6/063009 · Cited by 34
Boundary conditions and phase transitions in neural networks. Theoretical results
Jacques Demongeot, A., Jézéquel, C., & Sené, S. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.04.003 · Cited by 38
Controlling chaos in random Boolean networks
Luque, B., & Solé, R. V. (1997) · Europhysics Letters (EPL) · doi:10.1209/epl/i1997-00196-9 · Cited by 28
Self-citationRevisiting the edge of chaos: Again?
Teuscher, C. (2022) · Biosystems · doi:10.1016/j.biosystems.2022.104693 · Cited by 26
Reversible Boolean networks I: distribution of cycle lengths
Coppersmith, S. N., Kadanoff, L. P., & Zhang, Z. (2001) · Physica D Nonlinear Phenomena · doi:10.1016/s0167-2789(00)00198-6 · Cited by 24
Luque, B., & Ballesteros, F. (2004) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2004.04.080 · Cited by 23
Self-citationLópez-Díaz, A. J., Sánchez-Puig, F., & Gershenson, C. (2023) · Entropy · doi:10.3390/e25020254 · Cited by 21
Modular random Boolean networksPreprint
Poblanno-balp, R., & Gershenson, C. (2010) · Cited by 21
Heterogeneity extends criticality
Sánchez-Puig, F., Zapata, O., Pineda, O. K., Íñiguez, G., & Gershenson, C. (2023) · Frontiers in Complex Systems · doi:10.3389/fcpxs.2023.1111486 · Cited by 25
Inference of Boolean Networks Using Sensitivity Regularization
Liu, W., Lähdesmäki, H. J., Dougherty, E. R., & Ilya Shmulevich, C. (2008) · EURASIP Journal on Bioinformatics and Systems Biology · doi:10.1155/2008/780541 · Cited by 20
Modular Random Boolean Networks
Poblanno-Balp, R., & Gershenson, C. (2011) · Artificial Life · doi:10.1162/artl_a_00042 · Cited by 20
Self-Organized Criticality and Adaptation in Discrete Dynamical Networks
Rohlf, T., & Bornholdt, S. (2009) · Understanding complex systems · doi:10.1007/978-3-642-01284-6_5 · Cited by 20
Rohlf, T. (2008) · Max Planck Institute for Plasma Physics · Cited by 20
Self-organized critical random Boolean networks
Luque, B., Ballesteros, F., & Muro, E. M. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.63.051913 · Cited by 20
Self-citationEmergence of Geometric Turing Patterns in Complex Networks
van der Kolk, J., García-Pérez, G., Kouvaris, N. E., Serrano, M. Á., & Boguñá, M. (2023) · Physical Review X · doi:10.1103/physrevx.13.021038 · Cited by 20
Damage spreading in spatial and small-world random Boolean networks
Lu, Q., & Teuscher, C. (2014) · Physical Review E · doi:10.1103/physreve.89.022806 · Cited by 19
Szejka, A., & Drossel, B. (2010) · Physical Review E · doi:10.1103/physreve.81.021908 · Cited by 19
Dynamical Instability in Boolean Networks as a Percolation Problem
Squires, S., Ott, E., & Girvan, M. (2012) · Physical Review Letters · doi:10.1103/physrevlett.109.085701 · Cited by 18
Length of state cycles of random Boolean networks: an analytic study
Somogyvári, Z., & Payrits, S. (2000) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/33/38/304 · Cited by 18
Stability of Boolean networks: The joint effects of topology and update rules
Squires, S., Pomerance, A., Girvan, M., & Ott, E. (2014) · Physical Review E · doi:10.1103/physreve.90.022814 · Cited by 18
Generalizing Mechanistic Explanations Using Graph-Theoretic Representations
Bechtel, W. (2015) · History, philosophy and theory of the life sciences · doi:10.1007/978-94-017-9822-8_9 · Cited by 18
Measuring Mutual Information in Random Boolean NetworksPreprint
Luque, B., & Ferrera, A. (1999) · arXiv (Cornell University) · doi:10.48550/arxiv.adap-org/9909004 · Cited by 17
Self-citationRohlf, T. (2008) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/84/10004 · Cited by 16
Random Boolean networks response to external periodic signals
Ballesteros, F., & Luque, B. (2002) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(02)01002-6 · Cited by 15
Self-citationEvolution of Brains and Computers: The Roads Not Taken
Solé, R., & Seoane, L. F. (2022) · Entropy · doi:10.3390/e24050665 · Cited by 14
Self-citationOn the Criticality of Adaptive Boolean Network Robots
Braccini, M., Roli, A., Barbieri, E., & Kauffman, S. A. (2022) · Entropy · doi:10.3390/e24101368 · Cited by 14
Critical line in random-threshold networks with inhomogeneous thresholds
Rohlf, T. (2008) · Physical Review E · doi:10.1103/physreve.78.066118 · Cited by 14
Coppersmith, S. N., Kadanoff, L. P., & Zhang, Z. (2001) · Physica D Nonlinear Phenomena · doi:10.1016/s0167-2789(01)00286-x · Cited by 13
Rugged fitness landscapes of Kauffman models with a scale-free network
Iguchi, K., Kinoshita, S., & Yamada, H. S. (2005) · Physical Review E · doi:10.1103/physreve.72.061901 · Cited by 13
STRUCTURAL TENDENCIES — EFFECTS OF ADAPTIVE EVOLUTION OF COMPLEX (CHAOTIC) SYSTEMS
Gecow, A. (2008) · RePEc: Research Papers in Economics · Cited by 12
Percolation in bipartite Boolean networks and its role in sustaining life
Hannam, R., Kühn, R., & Annibale, A. (2019) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8121/ab3053 · Cited by 10
Effective Connectivity and Bias Entropy Improve Prediction of Dynamical Regime in Automata Networks
Costa, F. X., Rozum, J. C., Marcus, A. M., & Rocha, L. M. (2023) · Entropy · doi:10.3390/e25020374 · Cited by 10
Dynamical phase transitions in graph cellular automata
Behrens, F., Hudcová, B., & Zdeborová, L. (2024) · Physical review. E · doi:10.1103/physreve.109.044312 · Cited by 10
Choppala, P. B., Frean, M. R., & Teal, P. D. (2022) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2022.3146051 · Cited by 10
Liu, J., & Hu, B. (2008) · International Journal of Modelling Identification and Control · doi:10.1504/ijmic.2008.018182 · Cited by 10
Particle filter parallelisation using random network based resampling
Choppala, P. B., Teal, P. D., & Frean, M. R. (2014) · Cited by 8
Robustness quantification of hierarchical complex networks under targeted failures
Bilal, K., Manzano, M., Erbad, A., Calle, E., & Khan, S. U. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.09.008 · Cited by 8
Critical excitation-inhibition balance in dense neural networks
Baumgarten, L., & Bornholdt, S. (2019) · Physical review. E · doi:10.1103/physreve.100.010301 · Cited by 8
Kauffman Boolean model in undirected scale-free networks
Fronczak, P., Fronczak, A., & Hołyst, J. A. (2008) · Physical Review E · doi:10.1103/physreve.77.036119 · Cited by 8
Damage spreading and the Lyapunov spectrum of cellular automata and Boolean networks
Vispoel, M., Daly, A. J., & Baetens, J. M. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.114989 · Cited by 7
Gecow, A. (2011) · InTech eBooks · doi:10.5772/14485 · Cited by 6
Competitive cluster growth on networks: Complex dynamics and survival strategies
Thyagu, N. N., & Mehta, A. (2010) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.12.011 · Cited by 6
State concentration exponent as a measure of quickness in Kauffman-type networks
Amari, S., Ando, H., Toyoizumi, T., & Masuda, N. (2013) · Physical Review E · doi:10.1103/physreve.87.022814 · Cited by 6
A Novel Online Adaptation Mechanism in Artificial Systems Provides Phenotypic Plasticity
Braccini, M., Roli, A., & Kauffman, S. A. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23929-8_12 · Cited by 6
STRUCTURAL TENDENCIES — EFFECTS OF ADAPTIVE EVOLUTION OF COMPLEX (CHAOTIC) SYSTEMS
GECOW, A. (2008) · International Journal of Modern Physics C · doi:10.1142/s0129183108012418 · Cited by 6
Stability of linear Boolean networks
Chandrasekhar, K., Kadelka, C., Laubenbacher, R., & Murrugarra, D. (2023) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2023.133775 · Cited by 5
Order-disorder phase transition in random-walk networks
Ballesteros, F., & Luque, B. (2005) · Physical Review E · doi:10.1103/physreve.71.031104 · Cited by 5
Self-citationREVERSE ENGINEERING OF THE NK BOOLEAN NETWORK AND ITS EXTENSION — FUZZY LOGIC NETWORK
Cao, Y., Wang, P. P., & Resconi, G. (2007) · New Mathematics and Natural Computation · doi:10.1142/s179300570700063x · Cited by 5
Gecow, A. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1003.1988 · Cited by 5
Stability of Boolean networks with generalized canalizing rules
Pomerance, A., Girvan, M., & Ott, E. (2012) · Physical Review E · doi:10.1103/physreve.85.046106 · Cited by 4
Yang, G., Campbell, C., & Albert, R. (2016) · Physical review. E · doi:10.1103/physreve.94.062316 · Cited by 4
Complexity Threshold for Functioning Directed Networks in Damage Size DistributionPreprint
Gecow, A. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1004.3795 · Cited by 4
Landscapes, Gene Networks and Pattern Formation: On the Cambrian Explosion
Solé, R. V., Salazar‐Ciudad, I., & García‐Fernàndez, J. (1999) · Advances in Complex Systems · doi:10.1142/s0219525999000163 · Cited by 5
Self-citationFormation of the frozen core in critical Boolean NetworksPreprint
Möller, M., & Drossel, B. (2016) · Cited by 4
A Certain Complexity Threshold during Growth of Functioning Networks
Gecow, A. (2008) · International Conference on Complexity and Intelligence of the Artificial and Natural Complex Systems. Medical Applications of the Complex Systems. Biomedical Computing (CANS) · doi:10.1109/cans.2008.16 · Cited by 3
Adaptive Local Information Transfer in Random Boolean Networks
Haruna, T. (2017) · Artificial Life · doi:10.1162/artl_a_00224 · Cited by 3
Gecow, A., & Iantovics, L. B. (2022) · Symmetry · doi:10.3390/sym14010092 · Cited by 3
Gene Regulatory Network Modeling: A Data Driven Approach
Cao, Y., Wang, P. P., & Tokuta, A. (2007) · Springer eBooks · doi:10.1007/978-3-540-71258-9_12 · Cited by 3
Critical dynamics of randomly assembled and diluted threshold networks
Kürten, K. E., & Clark, J. W. (2008) · Physical Review E · doi:10.1103/physreve.77.046116 · Cited by 3
A Simplified Algorithm for Statistical Investigation of Damage Spreading
Gecow, A., Iantovics, L. B., Calin, E., & Filip, F. G. (2009) · AIP conference proceedings · doi:10.1063/1.3130616 · Cited by 3
Phase Transitions in Logic Networks
Sawhill, B. K., & Kauffman, S. A. (1997) · Cited by 3
Interplay between degree and Boolean rules in the stability of Boolean networks
Min, B. (2020) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0014191 · Cited by 3
Emergence of Chaos and Complexity During System Growth
Gecow, A. (2009) · Understanding complex systems · doi:10.1007/978-3-642-02199-2_5 · Cited by 2
Transition to chaos in models of genetic networks
Hill, C. C., Sawhill, B. K., Kauffman, S. A., & Glass, L. (2008) · Springer eBooks · doi:10.1007/bfb0105022 · Cited by 2
Critical line in undirected Kauffman Boolean networks — the role of percolation
Fronczak, P., & Fronczak, A. (2008) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8113/41/22/224009 · Cited by 2
Critical line in undirected Kauffman boolean networks- the role of percolationPreprint
Fronczak, P., & Fronczak, A. (2012) · Cited by 2
Dynamics of attractor transitions in Boolean networks under noise
Min, B., Choi, J., & Laubenbacher, R. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0286088 · Cited by 2
Chatterjee, S. (2015) · Probability Theory and Related Fields · doi:10.1007/s00440-015-0656-2 · Cited by 1
Classification of Discrete Dynamical Systems Based on Transients
Hudcová, B., & Mikolov, T. (2021) · Artificial Life · doi:10.1162/artl_a_00342 · Cited by 1
The Role of Dynamical Regimes of Online Adaptive BN-Robots in Noisy Environments
Braccini, M., Barbieri, E., & Roli, A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-31183-3_15 · Cited by 1
An Investigation of Graceful Degradation in Boolean Network Robots Subject to Online Adaptation
Braccini, M., Baldini, P., & Roli, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-57430-6_16 · Cited by 1
Self-overlap as a method of analysis in Ising models
Ferrera, A., Luque, B., Lacasa, L., & Valero, E. (2007) · Physical Review E · doi:10.1103/physreve.75.061103 · Cited by 1
Self-citationComparing the evolution of canalyzing and threshold networks
Priester, C., Szejka, A., & Drossel, B. (2011) · The European Physical Journal B · doi:10.1140/epjb/e2010-10300-1 · Cited by 1
Online adaptation in robots as biological development provides phenotypic plasticityPreprint
Braccini, M., Roli, A., & Kauffman, S. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.02367 · Cited by 1
The order-chaos phase transition for a general class of complex Boolean networksPreprint
Chatterjee, S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1310.4474
Metadata
- Authors
- Luque, B., & Solé, R. V.
- Affiliations
- Universidad Nacional Autónoma de México (MX)
- Santa Fe Institute (US)
- Venue
- Physical Review E
- Publisher
- American Physical Society (APS)
- Published
- 1997-01-01
- Volume
- 55
- Issue
- 1
- Pages
- 257-260
- ISSN
- 1063-651X (ISSN-L)
- 1095-3787
- OpenAlex
- W1992555597
- Cited by
- 111
- References
- 12
- Citations by year
- 2026: 2 · 2024: 5 · 2023: 6 · 2022: 6 · 2021: 2 · 2020: 2 · 2019: 2 · 2018: 1 · 2017: 1 · 2016: 4 · 2015: 2 · 2014: 4 · 2013: 3 · 2012: 3
- FWCI
- 2.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Cellular Automata and Applications
- Keywords
- random networks, phase transitions, critical points, Boolean networks, damage spreading, annealed approximation, spin glass
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