Fast analytical methods for finding significant labeled graph motifs

DOI10.1007/s10618-017-0544-8zbMath1411.68115OpenAlexW2766000990WikidataQ114827250 ScholiaQ114827250MaRDI QIDQ1741244

Misael Mongiovì, Alfredo Ferro, Dennis Shasha, Rosalba Giugno, Giovanni Micale, Alfredo Pulvirenti

Publication date: 3 May 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-017-0544-8

zbMATH Keywords

graph algorithmsrandom network modelsnetwork mininglabeled graph motifs

Mathematics Subject Classification ID

Random graphs (graph-theoretic aspects) (05C80) Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Graph algorithms (graph-theoretic aspects) (05C85) Systems biology, networks (92C42)

Uses Software

• GraphBase
• BiNGO
• ClueGO
• NeMoFinder

Cites Work

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• G-Tries: a data structure for storing and finding subgraphs
• Emergence of Scaling in Random Networks
• BOULWARE STATE IN EXACTLY SOLVABLE MODELS OF 2D DILATON GRAVITY
• Estimation and Prediction for Stochastic Blockstructures
• Analytical maximum-likelihood method to detect patterns in real networks
• The average distances in random graphs with given expected degrees

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