Network motifs: simple building blocks of complex networks.

By Ron Milo, S. Shen-Orr, Shalev Itzkovitz, Nadav Kashtan, Dmitri "Mitya" Chklovskii, Uri Alon. 2002

7,486 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistical and Nonlinear Physics, Computational Biology & Bioinformatics, Network Science & Complex Systems

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Citations in the corpus

Higher-order organization of complex networks (2016, neutral): “S63 References and Notes 1. R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, U. Alon, Network motifs: Simple building blocks of complex networks. Science 298, 824–827 (2002). Medline doi:10.1126/science.298.5594.824 2. S. Mangan, A. Zaslaver, U. Alon, The coherent feedforward loop serv”
Geometric Deep Learning: Going beyond Euclidean data (2016, methodology): “Instead of embedding the vertices, the graph structure can be processed by decomposing it into small sub-graphs called motifs [36] or graphlets [37].”
Community detection in graphs (2009, background): “They remarked that the fundamental unit to define modularity is the edge, but that high edge densities inside clusters usually imply the existence of long-range topological correlations between vertices, which are revealed by the presence of motifs [223], i.”
Cytoscape: a software environment for integrated models of biomolecular interaction networks. (2003, background): “The recent discovery of inherent higher-order modular architecture in cellular networks (12) will hopefully allow complexity reduction in visualization and analysis systems such as Cytoscape.”
Spatial Networks (2010, methodology): “The idea of motifs is particularly important for biological networks [231] but can be applied to any type of networks, including spatial networks.”
Complex photonics: Dynamics and applications of delay-coupled semiconductors lasers (2013, methodology): “real-life networks, as calculated by Milo et al. (2002): module 3”
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms (2006, positive): “True biological TRNs also contain specific structural motifs that are statistically overrepresented as compared to random graphs of the same in- and outdegree [ 17 ,18] (e.g.”
A social network perspective on measurements of dominance hierarchies (2012, background): “The relative frequencies of triadic configurations, or ‘motifs’ are routinely used to characterize the structural properties of networks (Kendall & Babington Smith 1940; Harary & Moser 1966; Holland & Leinhardt 1976; Frank & Harary 1982; Karlberg 1999; Milo et al. 2002; Faust 2008; Allesina & Levine”
Search for computational modules in the C. elegans brain (2004, methodology): “In order to avoid assigning significance to a N -neuron pattern just because it contains Ν−1 -neuron motifs we incorporate the Ν−1 -neuron statistics into the expectations used to search for N -neuron motifs (Milo et al., 2002).”