The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Emergence of Scaling in Random Networks
Open accessAbstract
Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual 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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Other topologically constrained networks such as transportation networks and internet routers face similar issues.6”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [6]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Like most social networks, the in-degree distribution in the #Ferguson network follows roughly a powerlaw, with a small number of nodes receiving a disproportionately high number of retweets and mentions, and most receiving none at all (Barabási & Albert, 1999).”
From #Ferguson is everywhere: initiators in emerging counterpublic networks (2016) · cites this paper as Barabási & Albert, 1999
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A network with the power-law distribution of degrees has the property of scale-freeness, showing that only few linguistic units have the strong general syntactic capacity to combine with other linguistic units (words), while most other linguistic units have weaker combining ability (Barabási and Albert, 1999).”
From Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels (2021) · cites this paper as Barabási and Albert, 1999
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The distribution of degrees of vertices in a complex network and a random network generally follows the power-law distribution and Poisson distribution, respectively (Barabási and Albert, 1999; Newman, 2003, 2010).”
From Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels (2021) · cites this paper as Barabási and Albert, 1999
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“complex network theory has demonstrated that many (non-quantum) systems exhibit similarities in their complex features 14 -18 , in the organization of their structure and dynamics 19 -24 , the controllability of their constituents 25 , and their resilience to structural and dynamical perturbations 26 -31 .”
From Complex networks from classical to quantum (2019) · cites this paper as [15]
The cited source says what the claim attributes to it.
In the citing paper
“Several models, based on mechanisms like preferential attachment 15 , fi tness 94 , or constrained random wiring 54 , to mention some of them, were developed to reproduce degree distributions commonly observed in empirical systems.”
From Complex networks from classical to quantum (2019) · cites this paper as [15]
The cited source says what the claim attributes to it.
In the citing paper
“Concurrent with the formulation of small-world networks, another feature found to be common in many realworld networks is the existence of a small number of highly connected nodes (called “hubs”).6”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [6]
The cited source says what the claim attributes to it.
In the citing paper
“For instance, when new nodes are connected preferentially to a highly connected “rich club,” the degree distribution matches that of other real-world networks, a scenario known as “the rich get richer.”6”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [6]
The cited source says what the claim attributes to it.
In the citing paper
“This network model could be generated using a simple process of sequentially adding nodes to a graph and preferentially attaching them to nodes that already had many connections (e.g., everyone wants to be friends with the popular person).The resulting degree distribution follows a power law.”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [6]
The cited source says what the claim attributes to it.
In the citing paper
“A network with the power-law distribution of degrees has the property of scale-freeness, showing that only few linguistic units have the strong general syntactic capacity to combine with other linguistic units (words), while most other linguistic units have weaker combining ability (Barabási and Albert, 1999).”
From Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels (2021) · cites this paper as Barabási and Albert, 1999
The cited source says what the claim attributes to it.
In the citing paper
“The distribution of degrees of vertices in a complex network and a random network generally follows the power-law distribution and Poisson distribution, respectively (Barabási and Albert, 1999; Newman, 2003, 2010).”
From Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels (2021) · cites this paper as Barabási and Albert, 1999
The cited source says what the claim attributes to it.
In the citing paper
“Traditionally complex network theory explains the evolution of scale-free phenomenon by Bgrowth^and Bpreferential attachment^ (Xie and Levinson 2009;Barabasi and Albert 1999).”
From A Network Approach to Link Visibility and Urban Activity Location (2018) · cites this paper as Barabasi and Albert 1999
In the citing paper
“A power law connectivity degree distribution is the defining feature of a scale-free network46,47”
From Descending networks transform command signals into population motor control (2024) · cites this paper as [46]
In the citing paper
“It has been found that despite the diversity of complex networks, ranging from the Internet to the protein interaction networks in the cell, most networks obey universal properties: they are small world [8], they are scale free [9], and they have a nontrivial community structure [7].”
From Interdisciplinary and physics challenges of network theory (2015) · cites this paper as [9]
In the citing paper
“Therefore, as long as δ < d − 2, the generalized degree increases dynamically due to an effective “linear preferential attachment24”, according to which the generalized degree of a δ-face increases at each time by one, with a probability increasing linearly with the current value of its generalized degree.”
From Complex Quantum Network Manifolds in Dimension d > 2 are Scale-Free (2015) · cites this paper as [24]
In the citing paper
“As our societies are increasingly instantiated in digital form, once-mathematical abstractions of social processes—networks are one prominent example—become very real parts of daily life”
From Stewardship of global collective behavior (2021) · cites this paper as [23]
In the citing paper
“Here, a better fit is achieved with a scale-free network32; however, neither model simultaneously fits both distributions.”
From A mesoscale connectome of the mouse brain (2014) · cites this paper as [32]
In the citing paper
“suggesting relatively heavier tails and higher inequality, indicating a greater tendency for the emergence of super-hubs-nodes with degrees larger than expected in the standard preferential attachment model (Barabási and Albert 1999).”
From "Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data (2025) · cites this paper as Barabási and Albert 1999
In the citing paper
“The preferential attachment model (Barabási and Albert 1999) is based on two mechanisms: growth, where a new node is added at each time step, and preferential attachment, where each new node connects to m existing nodes with a probability proportional to their degrees.”
From "Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data (2025) · cites this paper as Barabási and Albert 1999
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
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
Bollobás, B. (1985) · Cited by 6,196
(2017) · Sage eBooks · doi:10.4135/9781483381411.n575 · Cited by 6,067
Diameter of the World-Wide Web
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (1999) · Nature · doi:10.1038/43601 · Cited by 4,240
Self-citationMean-field theory for scale-free random networks
Barabási, A.-Ĺ., Albert, R., & Jeong, H. (1999) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(99)00291-5 · Cited by 2,261
Self-citationHow popular is your paper? An empirical study of the citation distribution
Redner, S. (1998) · The European Physical Journal B · doi:10.1007/s100510050359 · Cited by 1,509
Accessibility of information on the web
Lawrence, S., & Giles, C. L. (1999) · Nature · doi:10.1038/21987 · Cited by 1,371
Arthur, W. B. (1999) · Science · doi:10.1126/science.284.5411.107 · Cited by 1,147
Lawrence, S., & Giles, C. L. (1998) · Science · doi:10.1126/science.280.5360.98 · Cited by 975
Research works printed but not linked: 3
- Rényi A., Publ. Math. Inst. Hung. Acad. Sci. 5, 17 (1960);
- Milgram S., Psychol. Today 2, 60 (1967);
- Barthélémy M., Amaral L. A. N., Phys. Rev. Lett. 82, 15 (1999).
Cited by
Sorted by citations
Items 501–600 of 1,152
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective DesignsPreprint
Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., & Koutra, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.11468 · Cited by 512
The implications of human metabolic network topology for disease comorbidity
Lee, D., Park, J., Kay, K. A., Christakis, N. A., Oltvai, Z. N., & Barabási, A.-Ĺ. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0802208105 · Cited by 495
Interneuron Diversity series: Circuit complexity and axon wiring economy of cortical interneurons
Buzsáki, G., Geisler, C. E., Henze, D. A., & Wang, X. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.02.007 · Cited by 498
A complex network approach to supply chain network theory
Hearnshaw, E. J. S., & Wilson, M. M. J. (2013) · International Journal of Operations & Production Management · doi:10.1108/01443571311307343 · Cited by 525
Dall, J., & Christensen, M. (2002) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.66.016121 · Cited by 489
Hypergraphs and Cellular Networks
Klamt, S., Haus, U., & Theis, F. J. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000385 · Cited by 473
DAGs with NO TEARS: Continuous Optimization for Structure LearningPreprint
Zheng, X., Aragam, B., Ravikumar, P., & Xing, E. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.01422 · Cited by 488
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
Hattori, M., Okuno, Y., Goto, S., & Kanehisa, M. I. (2003) · Journal of the American Chemical Society · doi:10.1021/ja036030u · Cited by 487
Galeotti, A., Goyal, S., Jackson, M. O., Vega‐Redondo, F., & Yariv, L. (2009) · The Review of Economic Studies · doi:10.1111/j.1467-937x.2009.00570.x · Cited by 473
Efficiency of scale-free networks: error and attack tolerance
Crucitti, P., Latora, V., Massimo Marchiori, B., & Rapisarda, A. (2003) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(02)01545-5 · Cited by 486
High‐Betweenness Proteins in the Yeast Protein Interaction Network
Joy, M. P., Brock, A., Ingber, D. E., & Huang, S. (2005) · BioMed Research International · doi:10.1155/jbb.2005.96 · Cited by 483
Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems
Cha, M., Kwak, H., Rodríguez, P., Ahn, Y., & Moon, S. B. (2009) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2008.2011358 · Cited by 478
Organizing Far from Equilibrium: Nonlinear Change in Organizational Fields
Meyer, A. D., Gaba, V., & Colwell, K. A. (2005) · Organization Science · doi:10.1287/orsc.1050.0135 · Cited by 485
Integrating Personality Structure, Personality Process, and Personality Development
Baumert, A., Schmitt, M., Perugini, M., Johnson, W., Blum, G. S., Borkenau, P., Costantini, G., Denissen, J. J. A., Fleeson, W., Grafton, B., et al. (2017) · European Journal of Personality · doi:10.1002/per.2115 · Cited by 549
Intrinsic vs. Image-Related Utility in Social Media: Why Do People Contribute Content to Twitter?
Toubia, O., & Stephen, A. T. (2013) · Marketing Science · doi:10.1287/mksc.2013.0773 · Cited by 484
Fitting Heavy Tailed Distributions: The poweRlaw Package
Gillespie, C. S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v064.i02 · Cited by 483
4. A Relational Event Framework for Social Action
Butts, C. T. (2008) · Sociological Methodology · doi:10.1111/j.1467-9531.2008.00203.x · Cited by 482
Mining Heterogeneous Information Networks: Principles and Methodologies
Sun, Y., & Han, J. (2012) · Synthesis lectures on data mining and knowledge discovery · doi:10.2200/s00433ed1v01y201207dmk005 · Cited by 474
The spatial structure of networks
Gastner, M. T., & Newman, M. E. J. (2006) · The European Physical Journal B · doi:10.1140/epjb/e2006-00046-8 · Cited by 481
Alexander-Bloch, A. F., Gogtay, N., Meunier, D., Birn, R., Clasen, L., Lalonde, F., Lenroot, R., Giedd, J., & Bullmore, E. T. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00147 · Cited by 480
A mixture model for random graphs
Daudin, J., Picard, F., & Robin, S. S. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9046-7 · Cited by 480
Superfamily phenomena and motifs of networks induced from time series
Xu, X.-K., Zhang, J., & SMALL, M. P. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806082105 · Cited by 478
Cramer, A. O. J., van der Sluis, S., Noordhof, A., Wichers, M., Geschwind, N., Aggen, S. H., Kendler, K. S., & Borsboom, D. (2012) · European Journal of Personality · doi:10.1002/per.1866 · Cited by 512
Synchronous Bursts on Scale-Free Neuronal Networks with Attractive and Repulsive Coupling
Wang, Q., Chen, G., & Perc, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0015851 · Cited by 477
hnRNP K: One protein multiple processes
Bomsztyk, K., Denisenko, O. N., & Ostrowski, J. (2004) · BioEssays · doi:10.1002/bies.20048 · Cited by 482
The “robust yet fragile” nature of the Internet
Doyle, J. C., Alderson, D., Li, L., Low, S. H., Roughan, M., Shalunov, S., Tanaka, R., & Willinger, W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0501426102 · Cited by 470
Modeling of Protein Interaction Networks
Vázquez, A., Flammini, A., Maritan, A., & Vespignani, A. (2002) · Complexus · doi:10.1159/000067642 · Cited by 475
Co-evolution of Firms, Industries and Networks in Space
Wal, A. L. J. T., & Boschma, R. A. (2009) · Regional Studies · doi:10.1080/00343400802662658 · Cited by 474
Attack Robustness and Centrality of Complex Networks
Iyer, S., Killingback, T., Sundaram, B., & Wang, Z. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059613 · Cited by 479
Mathematical results on scale‐free random graphs
Bollobás, B., & Riordan, O. M. (2002) · doi:10.1002/3527602755.ch1 · Cited by 471
The Spatial Evolution of Innovation Networks: A Proximity Perspective
Boschma, R. A., & Frenken, K. (2010) · Edward Elgar Publishing eBooks · doi:10.4337/9781849806497.00012 · Cited by 467
Analysis of the airport network of India as a complex weighted network
Bagler, G. (2008) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2008.01.077 · Cited by 481
Can we rationally design promiscuous drugs?
HOPKINS, A., MASON, J., & OVERINGTON, J. (2006) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2006.01.013 · Cited by 469
Deterministic scale-free networks
Barabási, A.-Ĺ., Regan, E. R., & Vicsek, T. (2001) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(01)00369-7 · Cited by 468
Gaining confidence in high-throughput protein interaction networks
Bader, J. S., Chaudhuri, A., Rothberg, J. M., & Chant, J. (2003) · Nature Biotechnology · doi:10.1038/nbt924 · Cited by 466
Yook, S., Jeong, H., Barabási, A.-Ĺ., & Tu, Y. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.5835 · Cited by 467
Zac1 Regulates an Imprinted Gene Network Critically Involved in the Control of Embryonic Growth
Varrault, A., Gueydan, C., Delalbre, A., Bellmann, A., Houssami, S., Aknin, C., Séverac, D., Chotard, L., Kahli, M., Digarcher, A. L., et al. (2006) · Developmental Cell · doi:10.1016/j.devcel.2006.09.003 · Cited by 478
Is the Boston Subway a Small-World Network?Preprint
Latora, V., & Massimo Marchiori, B. (2002) · Cited by 467
Statistical Models for Social Networks
Snijders, T. A. B. (2011) · Annual Review of Sociology · doi:10.1146/annurev.soc.012809.102709 · Cited by 482
Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight
Ghazalpour, A., Doss, S., Zhang, B., Wang, S., Plaisier, C., Castellanos, R., Brozell, A., Schadt, E. E., Drake, T. A., Lusis, A. J., et al. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020130 · Cited by 462
Synchronization is Enhanced in Weighted Complex Networks
Chávez, M., Hwang, D., Amann, A., Hentschel, H. G. E., & Boccaletti, S. (2005) · Physical Review Letters · doi:10.1103/physrevlett.94.218701 · Cited by 462
Assessing the role of network topology in transportation network resilience
Zhang, X., Miller-Hooks, E. D., & Denny, K. (2015) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2015.05.006 · Cited by 460
Aiding the detection of fake accounts in large scale social online services
Cao, Q., Sirivianos, M., Yang, X., & Pregueiro, T. (2012) · Cited by 449
Myers, C. R. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.68.046116 · Cited by 462
Onset of traffic congestion in complex networks
Zhao, L., Lai, Y., Park, K., & Ye, N. (2005) · Physical Review E · doi:10.1103/physreve.71.026125 · Cited by 458
Geographical dispersal of mobile communication networks
Lambiotte, R., Blondel, V. D., de Kerchove, C., Huens, E., Prieur, C., Smoreda, Z., & Dooren, P. M. V. (2008) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2008.05.014 · Cited by 458
Winners don't take all: Characterizing the competition for links on the web
Pennock, D. M., Flake, G. W., Lawrence, S., Glover, E. J., & Giles, C. L. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.032085699 · Cited by 458
Class of correlated random networks with hidden variables
Boguñá, M., & Pastor‐Satorras, R. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.68.036112 · Cited by 457
1. Networked Politics: Agency, Power, and Governance
Kahler, M. (2017) · Cornell University Press eBooks · doi:10.7591/9780801458880-003 · Cited by 456
Understanding network concepts in modules
Dong, J., & Horvath, S. (2007) · BMC Systems Biology · doi:10.1186/1752-0509-1-24 · Cited by 455
Topology of correlation-based minimal spanning trees in real and model markets
Bonanno, G., Caldarelli, G., Lillo, F., & Mantegna, R. N. (2003) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.68.046130 · Cited by 455
Fixed-Time Synchronization of Complex Networks With Impulsive Effects via Nonchattering Control
Yang, X., Lam, J., Ho, D. W. C., & Feng, Z. (2017) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2017.2691303 · Cited by 455
Scale-free networks of earthquakes and aftershocks
Baiesi, M., & Paczuski, M. (2004) · Physical Review E · doi:10.1103/physreve.69.066106 · Cited by 453
Imitation dynamics of vaccination behaviour on social networks
Fu, F., Rosenbloom, D. I. S., Wang, L., & Nowak, M. A. (2010) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2010.1107 · Cited by 456
Economics : an emerging small world?
Moraga‐González, J. L., van der Leij, M., & Goyal, S. (2004) · Econstor (Econstor) · Cited by 453
The worldwide maritime network of container shipping: spatial structure and regional dynamics
Ducruet, C., & Notteboom, T. (2012) · Global Networks · doi:10.1111/j.1471-0374.2011.00355.x · Cited by 452
The Economic Consequences of Social-Network Structure
Jackson, M. O., Rogers, B. W., & Zénou, Y. (2017) · Journal of Economic Literature · doi:10.1257/jel.20150694 · Cited by 486
Are randomly grown graphs really random?
Callaway, D. S., Hopcroft, J. E., Kleinberg, J. M., Newman, M. E. J., & Strogatz, S. H. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.64.041902 · Cited by 451
Inferring Networks of Diffusion and Influence
Gomez-Rodriguez, M., Leskovec, J., & Krause, A. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2086737.2086741 · Cited by 696
Streaming graph partitioning for large distributed graphs
Stanton, I., & Kliot, G. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339722 · Cited by 449
Ranking spreaders by decomposing complex networks
Zeng, A., & Zhang, C. (2013) · Physics Letters A · doi:10.1016/j.physleta.2013.02.039 · Cited by 441
The IBD interactome: an integrated view of aetiology, pathogenesis and therapy
de Souza, H. S. P., Fiocchi, C., & Iliopoulos, D. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.110 · Cited by 447
Prediction of Information Diffusion Probabilities for Independent Cascade Model
Saito, K., Nakano, R., & Kimura, M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85567-5_9 · Cited by 447
Distributed Consensus Filtering in Sensor Networks
Yu, W., Chen, G., Wang, Z., & Yang, W. D. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2021254 · Cited by 447
Statistical properties of sampled networks
Lee, S. H., Kim, P., & Jeong, H. (2006) · Physical Review E · doi:10.1103/physreve.73.016102 · Cited by 446
Li, H., Wang, Y., Zhang, D., Zhang, M., & Chang, E. Y. (2008) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1454008.1454027 · Cited by 451
Learning Deep Generative Models of GraphsPreprint
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., & Battaglia, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.03324 · Cited by 679
Recent advances in percolation theory and its applications
Saberi, A. A. (2015) · Physics Reports · doi:10.1016/j.physrep.2015.03.003 · Cited by 444
Turing patterns in network-organized activator–inhibitor systems
Nakao, H., & Mikhailov, A. S. (2010) · Nature Physics · doi:10.1038/nphys1651 · Cited by 429
Artificial intelligence in cancer target identification and drug discovery
You, Y., Lai, X., Pan, Y. F., Zheng, H., Vera, J., Liu, S., Deng, S., & Zhang, L. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00994-0 · Cited by 499
Social network theory: new insights and issues for behavioral ecologists
Sih, A., Hanser, S. F., & McHugh, K. (2009) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-009-0725-6 · Cited by 459
The physics of brain network structure, function and control
Lynn, C. W., & Bassett, D. S. (2019) · Nature Reviews Physics · doi:10.1038/s42254-019-0040-8 · Cited by 452
Controllability of complex networks via pinning
Sorrentino, F., di Bernardo, M., Garofalo, F., & Chen, G. (2007) · Physical Review E · doi:10.1103/physreve.75.046103 · Cited by 441
Synchronization on small-world networks
Hong, H., Choi, M. Y., & Kim, B. J. (2002) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.65.026139 · Cited by 441
Self-organized criticality as a fundamental property of neural systems
Hesse, J., & Gross, T. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00166 · Cited by 440
Evolution of networks with aging of sites
Dorogovtsev, S. N., & Mendes, J. F. F. (2000) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.62.1842 · Cited by 440
Statistical analysis of 22 public transport networks in Poland
Sienkiewicz, J., & Hołyst, J. A. (2005) · Physical Review E · doi:10.1103/physreve.72.046127 · Cited by 442
The complexity and robustness of metro networks
Derrible, S., & Kennedy, C. A. (2010) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.04.008 · Cited by 440
On Pinning Synchronization of Directed and Undirected Complex Dynamical Networks
Song, Q., & Cao, J. (2009) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2009.2024971 · Cited by 440
The Prevalence of Lying in America: Three Studies of Self-Reported Lies
Serota, K. B., Levine, T. R., & Boster, F. J. (2009) · Human Communication Research · doi:10.1111/j.1468-2958.2009.01366.x · Cited by 440
Power Laws in Economics: An Introduction
Gabaix, X. (2016) · The Journal of Economic Perspectives · doi:10.1257/jep.30.1.185 · Cited by 440
Cascade of failures in coupled network systems with multiple support-dependence relations
Shao, J., Buldyrev, S. V., Havlin, S., & Stanley, E. (2011) · Physical Review E · doi:10.1103/physreve.83.036116 · Cited by 439
Association between Functional Connectivity Hubs and Brain Networks
Tomasi, D. G., & Volkow, N. D. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq268 · Cited by 441
The dynamics of animal social networks: analytical, conceptual, and theoretical advances
Pinter‐Wollman, N., Hobson, E. A., Smith, J. E., Edelman, A. J., Shizuka, D., de Silva, S. L., Waters, J. S., Prager, S. D., Sasaki, T., Wittemyer, G., et al. (2013) · Behavioral Ecology · doi:10.1093/beheco/art047 · Cited by 458
Joint estimation of multiple graphical models
Guo, J., Levina, E., Michailidis, G. C., & Zhu, J. (2011) · Biometrika · doi:10.1093/biomet/asq060 · Cited by 438
Structure and Evolution of Online Social Networks
Kumar, R., Novak, J., & Tomkins, A. (2010) · Springer eBooks · doi:10.1007/978-1-4419-6515-8_13 · Cited by 438
The origin of power laws in Internet topologies revisited
Chen, Q., Chang, H., Govindan, R., & Jamin, S. (2003) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2002.1019306 · Cited by 437
Concentration Inequalities and Martingale Inequalities: A Survey
Chung, F., & Lü, L. (2006) · Internet Mathematics · doi:10.1080/15427951.2006.10129115 · Cited by 437
Craigen, D., Diakun-Thibault, N., & Purse, R. (2014) · Technology Innovation Management Review · doi:10.22215/timreview/835 · Cited by 437
Economic geography and the evolution of networks
Glückler, J. (2007) · Journal of Economic Geography · doi:10.1093/jeg/lbm023 · Cited by 436
Small-world human brain networks: Perspectives and challenges
Liao, X., Vasilakos, A. V., & Yong He, A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.03.018 · Cited by 443
Nonlinear dynamics and chaos in fractional-order neural networks
Kaslik, É., & Sivasundaram, S. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.030 · Cited by 440
Is There an Elite Hold? Traditional Media to Social Media Agenda Setting Influence in Blog Networks
Meraz, S. (2009) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2009.01458.x · Cited by 471
Symptoms of Complexity in a Tourism System
Baggio, R. (2008) · Tourism Analysis · doi:10.3727/108354208784548797 · Cited by 433
Controlling edge dynamics in complex networks
Nepusz, T., & Vicsek, T. (2012) · Nature Physics · doi:10.1038/nphys2327 · Cited by 422
Hijacking #myNYPD: Social Media Dissent and Networked Counterpublics
Jackson, S. J., & Welles, B. F. (2015) · Journal of Communication · doi:10.1111/jcom.12185 · Cited by 468
Finding key players in complex networks through deep reinforcement learning
Fan, C., Zeng, L., Sun, Y., & Liu, Y. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0177-2 · Cited by 436
Networks in FinancePreprint
Allen, F., & Babus, A. (2008) · SSRN Electronic Journal · doi:10.2139/ssrn.1094883 · Cited by 432
Spectral methods for community detection and graph partitioning
Newman, M. E. J. (2013) · Physical Review E · doi:10.1103/physreve.88.042822 · Cited by 433
Metadata
- Authors
- Barabási, A.-L., & Albert, R.
- Affiliations
- Department of Physics, University of Notre Dame, Notre Dame, IN 46556, USA.
- Venue
- Science
- Published
- 1999-10-15
- Volume
- 286
- Issue
- 5439
- Pages
- 509-512
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2008620264
- Cited by
- 36,135
- References
- 19
- Citations by year
- 2026: 1122 · 2025: 1428 · 2024: 1382 · 2023: 1410 · 2022: 1628 · 2021: 1838 · 2020: 1757 · 2019: 1878 · 2018: 1959 · 2017: 1885 · 2016: 1886 · 2015: 1997 · 2014: 2037 · 2013: 1914 · 2012: 1848
- FWCI
- 104.59
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Keywords
- scale-free networks, power-law degree distribution, random networks, preferential attachment, network expansion, self-organization, complex network topology, vertex connectivity
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
