Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs

By Nadav Kashtan, Shalev Itzkovitz, Ron Milo, Uri Alon. Bioinform., 2004

591 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/84ea9e676647daa5eaa5fbc0ed80d8cd5463d6dc

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In Molecular Biology

In Statistical and Nonlinear Physics

Citations in the corpus

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “Networkmotifs are circuits of 3 to 6 nodes in directed networks that are highly overrepresented as compared to randomized networks (Milo et al., 2002; Kashtan et al., 2004).”
Analysis of Biological Networks (2008, methodology): “This name is not precise enough as other betweenness measures exist [7,16,37].”
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting (2020, methodology): “As a side note, approximate counting algorithms are also widely used, especially for counting frequent network motifs [58], [59], [60], [61], and can provide a considerable”
Biological network comparison using graphlet degree distribution (2007, unknown): “Exhaustively computing the differences between networks is computationally infeasible, and thus efficient heuristic algorithms have been sought (16; 28).”
Functional atlas of the integrin adhesome (2007, methodology): “To detect network motifs in the adhesome, we applied the Mfinder softwar”
Network motifs: theory and experimental approaches (2007, background): “Detection of network motifs Open-source software that can detect network motifs is availabl”
The dynamics of animal social networks: analytical, conceptual, and theoretical advances (2014, unknown): “Other software specifically designed for motif analysis are also widely available: Mfinder (Kashtan et al.”
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction (2021, methodology): “First, existing motif mining techniques could not be directly utilized to derive expressive motifs for molecular graphs because they only rely on the discrete count of subgraph structures and overlook the chemical validity [1, 18].”
Identification of functional modules using network topology and high-throughput data (2007, methodology): “, Pm of equal size k was randomly selected as in [49].”
Network Motif Discovery Using Subgraph Enumeration and Symmetry-Breaking (2007, methodology): “Two basic techniques have been proposed for identifying network motifs: exact counting [18] and subgraph sampling [1,9,16].”