The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Superfamilies of Evolved and Designed Networks
Abstract
Complex biological, technological, and sociological networks can be of very different sizes and connectivities, making it difficult to compare their structures. Here we present an approach to systematically study similarity in the local structure of networks, based on the significance profile (SP) of small subgraphs in the network compared to randomized networks. We find several superfamilies of previously unrelated networks with very similar SPs. One superfamily, including transcription networks of microorganisms, represents “rate-limited” information-processing networks strongly constrained by the response time of their components. A distinct superfamily includes protein signaling, developmental genetic networks, and neuronal wiring. Additional superfamilies include power grids, protein-structure networks and geometric networks, World Wide Web links and social networks, and word-adjacency networks from different languages.
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
Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Network motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
Self-citationTranscriptional Regulatory Networks in Saccharomyces cerevisiae
Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., et al. (2002) · Science · doi:10.1126/science.1075090 · Cited by 2,956
A genomic regulatory network for development
Davidson, E. H., Rast, J. P., Oliveri, P., Ransick, A., Calestani, C., Yuh, C.-H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C., et al. (2002) · Science · doi:10.1126/science.1069883 · Cited by 1,567
Response Delays and the Structure of Transcription Networks
Rosenfeld, N., & Alon, U. (2003) · Journal of Molecular Biology · doi:10.1016/s0022-2836(03)00506-0 · Cited by 148
Self-citation
Cited by
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
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284
Network motifs: theory and experimental approaches
Alon, U. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2102 · Cited by 3,279
Self-citationOrganization, development and function of complex brain networks
SPORNS, O., CHIALVO, D., KAISER, M., & HILGETAG, C. (2004) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2004.07.008 · Cited by 2,055
Interactome Networks and Human Disease
Vidal, M., Cusick, M. E., & Barabási, A.-L. (2011) · Cell · doi:10.1016/j.cell.2011.02.016 · Cited by 1,771
Highly nonrandom features of synaptic connectivity in local cortical circuits
Song, S., Sjöström, P. J., Reigl, M., Nelson, S., & Chklovskii, D. B. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030068 · Cited by 1,512
Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence
Humphries, M. D., & Gurney, K. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002051 · Cited by 1,442
Identification and classification of hubs in brain networks
Sporns, O., Honey, C. J., & Kötter, R. (2007) · PLoS ONE · doi:10.1371/journal.pone.0001049 · Cited by 1,165
Intensity and coherence of motifs in weighted complex networks
Onnela, J.-P., Saramäki, J., Kertész, J., & Kaski, K. (2005) · Physical Review E · doi:10.1103/physreve.71.065103 · Cited by 1,112
Synthetic biology: new engineering rules for an emerging discipline
Andrianantoandro, E., Basu, S., Karig, D. K., & Weiss, R. (2006) · Molecular Systems Biology · doi:10.1038/msb4100073 · Cited by 1,081
Yu, H., Kim, P. M., Sprecher, E., Trifonov, V., & Gerstein, M. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030059 · Cited by 1,014
Broido, A. D., & Clauset, A. (2019) · Nature Communications · doi:10.1038/s41467-019-08746-5 · Cited by 875
Biological network comparison using graphlet degree distribution
Pržulj, N. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btl301 · Cited by 837
Protein-Protein Interaction Detection: Methods and Analysis
Rao, V. S., Srinivas, K., Sujini, G. N., & Kumar, G. N. S. (2014) · International Journal of Proteomics · doi:10.1155/2014/147648 · Cited by 811
Analyzing and modeling real-world phenomena with complex networks: a survey of applications
Costa, L. D. F., Oliveira, O. N., Travieso, G., Rodrigues, F. A., Villas Boas, P. R., Antiqueira, L., Viana, M. P., & Correa Rocha, L. E. (2011) · Advances in Physics · doi:10.1080/00018732.2011.572452 · Cited by 800
Automated reverse engineering of nonlinear dynamical systems
Bongard, J., & Lipson, H. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0609476104 · Cited by 792
FANMOD: a tool for fast network motif detection
Wernicke, S., & Rasche, F. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl038 · Cited by 745
Knowledge sharing and yahoo answers: everyone knows something
Adamic, L. A., Zhang, J., Bakshy, E., & Ackerman, M. S. (2008) · Proceedings of the 17th international conference on World Wide Web · doi:10.1145/1367497.1367587 · Cited by 732
Modeling interactome: scale-free or geometric?
Pržulj, N., Corneil, D. G., & Jurisica, I. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth436 · Cited by 706
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
Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs
Kashtan, N., Itzkovitz, S., Milo, R., & Alon, U. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth163 · Cited by 537
Self-citationModeling T Cell Antigen Discrimination Based on Feedback Control of Digital ERK Responses
Altan-Bonnet, G., & Germain, R. N. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030356 · Cited by 468
Circuitry and Dynamics of Human Transcription Factor Regulatory Networks
Neph, S., Stergachis, A. B., Reynolds, A., Sandstrom, R., Borenstein, E., & Stamatoyannopoulos, J. A. (2012) · Cell · doi:10.1016/j.cell.2012.04.040 · Cited by 467
Classes of complex networks defined by role-to-role connectivity profiles
Guimerà, R., Sales-Pardo, M., & Amaral, L. A. N. (2007) · Nature Physics · doi:10.1038/nphys489 · Cited by 425
Graph theory and networks in Biology
Mason, O., & Verwoerd, M. (2007) · IET Systems Biology · doi:10.1049/iet-syb:20060038 · Cited by 398
Connectivity for conservation: a framework to classify network measures
Rayfield, B., Fortin, M.-J., & Fall, A. (2011) · Ecology · doi:10.1890/09-2190.1 · Cited by 391
Control Profiles of Complex Networks
Ruths, J., & Ruths, D. (2014) · Science · doi:10.1126/science.1242063 · Cited by 391
A cellular and regulatory map of the cholinergic nervous system of C. elegans
Pereira, L., Kratsios, P., Serrano-Saiz, E., Sheftel, H., Mayo, A. E., Hall, D. H., White, J. G., LeBoeuf, B., Garcia, L. R., Alon, U., et al. (2015) · eLife · doi:10.7554/elife.12432 · Cited by 365
Self-citationThe Rich Club of the C. elegans Neuronal Connectome
Towlson, E. K., Vértes, P. E., Ahnert, S. E., Schafer, W. R., & Bullmore, E. T. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3784-12.2013 · Cited by 343
“Many types of realworld networks, including the C. elegans neuronal network, have been classified according to their motif frequency profiles (Milo et al., 2002, 2004; Sporns and Kötter, 2004).”
BackgroundRobustness of community structure in networks
Karrer, B., Levina, E., & Newman, M. E. J. (2008) · Physical Review E · doi:10.1103/physreve.77.046119 · Cited by 350
Morgan, R. J., & Soltesz, I. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0801372105 · Cited by 347
Analysis of a large-scale weighted network of one-to-one human communication
Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Menezes, M. A. D., Kaski, K., Barabási, A.-L., & Kertész, J. (2007) · New Journal of Physics · doi:10.1088/1367-2630/9/6/179 · Cited by 344
Subgraph centrality and clustering in complex hyper-networks
Estrada, E., & Rodríguez-Velázquez, J. A. (2006) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2005.12.002 · Cited by 323
The Incoherent Feed-forward Loop Accelerates the Response-time of the gal System of Escherichia coli
Mangan, S., Itzkovitz, S., Zaslaver, A., & Alon, U. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2005.12.003 · Cited by 323
Self-citationLink prediction in weighted networks: The role of weak ties
Lü, L., & Zhou, T. (2010) · EPL (Europhysics Letters) · doi:10.1209/0295-5075/89/18001 · Cited by 320
Data based identification and prediction of nonlinear and complex dynamical systems
Wang, W.-X., Lai, Y.-C., & Grebogi, C. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.06.004 · Cited by 299
Alvarez-Buylla, E. R., Benítez, M., Corvera-Poiré, A., Chaos Cador, Á., de Folter, S., Gamboa de Buen, A., Garay-Arroyo, A., García-Ponce, B., Jaimes-Miranda, F., Pérez-Ruiz, R. V., et al. (2010) · The Arabidopsis Book · doi:10.1199/tab.0127 · Cited by 298
Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking
Grochow, J. A., & Kellis, M. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-71681-5_7 · Cited by 297
Berners-Lee, T., Hall, W., Hendler, J., Shadbolt, N., & Weitzner, D. J. (2006) · Science · doi:10.1126/science.1126902 · Cited by 292
Graph Metrics for Temporal Networks
Nicosia, V., Tang, J., Mascolo, C., Musolesi, M., Russo, G., & Latora, V. (2013) · Understanding Complex Systems · doi:10.1007/978-3-642-36461-7_2 · Cited by 269
Network motifs: Structure does not determine function.
Ingram, P. J., Stumpf, M. P., & Stark, J. (2006) · BMC Genomics · doi:10.1186/1471-2164-7-108 · Cited by 267
Hierarchy Measure for Complex Networks
Mones, E., Vicsek, L., & Vicsek, T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033799 · Cited by 259
Integrative network alignment reveals large regions of global network similarity in yeast and human
Kuchaiev, O., & Pržulj, N. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr127 · Cited by 256
Structure-based control of complex networks with nonlinear dynamics
Zañudo, J. G. T., Yang, G., & Albert, R. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1617387114 · Cited by 256
Artzy-Randrup, Y., Fleishman, S. J., Ben-Tal, N., & Stone, L. (2004) · Science · doi:10.1126/science.1099334 · Cited by 253
Configuring Random Graph Models with Fixed Degree Sequences
Fosdick, B. K., Larremore, D. B., Nishimura, J., & Ugander, J. (2018) · SIAM Review · doi:10.1137/16m1087175 · Cited by 246
The Powerful Law of the Power Law and Other Myths in Network Biology
Lima-Mendez, G., & van Helden, J. (2009) · Molecular BioSystems · doi:10.1039/b908681a · Cited by 210
Conservation of trans-acting circuitry during mammalian regulatory evolution
Stergachis, A. B., Neph, S., Sandstrom, R., Haugen, E., Reynolds, A. P., Zhang, M., Byron, R., Canfield, T., Stelhing-Sun, S., Lee, K., et al. (2014) · Nature · doi:10.1038/nature13972 · Cited by 212
Spontaneous emergence of modularity in cellular networks
Solé, R. V., & Valverde, S. (2008) · Journal of The Royal Society Interface · doi:10.1098/rsif.2007.1108 · Cited by 102
neuroVIISAS: Approaching Multiscale Simulation of the Rat Connectome
Schmitt, O., & Eipert, P. (2012) · Neuroinformatics · doi:10.1007/s12021-012-9141-6 · Cited by 51
The Architecture of the Human RNA-Binding Protein Regulatory Network
Quattrone, A., & Dassi, E. (2019) · iScience · doi:10.1016/j.isci.2019.10.058 · Cited by 53
Wang, H., Chen, C., & Fushing, H. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045502 · Cited by 33
Convergent Evolutionary Paths in Biological and Technological Networks
Solé, R. V., Valverde, S., & Rodriguez-Caso, C. (2011) · Evolution: Education and Outreach · doi:10.1007/s12052-011-0346-1 · Cited by 20
Hierarchy in directed random networks
Mones, E. (2013) · Physical Review E · doi:10.1103/physreve.87.022817 · Cited by 16
Early Systems Biology and Prebiotic Networks
Shenhav, B., Solomon, A., Lancet, D., & Kafri, R. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-32126-2_2 · Cited by 13
Huang, C.-H., Tsai, J. J. P., Kurubanjerdjit, N., & Ng, K.-L. (2019) · doi:10.1101/536318 · Cited by 4
Theoretical Analysis of Resource-Induced Phase Transitions in Estimation Strategies
Tottori, T., & Kobayashi, T. J. (2026) · Physical Review Letters · doi:10.1103/5ynb-7k4v
Metadata
- Authors
- Milo, R., Itzkovitz, S., Kashtan, N., Levitt, R., Shen-Orr, S., Ayzenshtat, I., Sheffer, M., & Alon, U.
- Venue
- Science
- Published
- 2004-03-05
- Volume
- 303
- Issue
- 5663
- Pages
- 1538-1542
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W1995907191
- Cited by
- 1,412
- References
- 5
- FWCI
- 49.35
- Type
- journal article
- Language
- en
- Open access
- closed
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.