Searching and inferring colorful topological motifs in vertex-colored graphs

Diego P. Rubert (), Eloi Araujo (), Marco A. Stefanes (), Jens Stoye () and Fábio V. Martinez ()
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Diego P. Rubert: Universidade Federal de Mato Grosso do Sul
Eloi Araujo: Universidade Federal de Mato Grosso do Sul
Marco A. Stefanes: Universidade Federal de Mato Grosso do Sul
Jens Stoye: Bielefeld University
Fábio V. Martinez: Universidade Federal de Mato Grosso do Sul

Journal of Combinatorial Optimization, 2020, vol. 40, issue 2, No 5, 379-411

Abstract: Abstract The analysis of biological networks allows the understanding of many biological processes, including the structure, function, interaction and evolutionary relationships of their components. One of the most important concepts in biological network analysis is that of network motifs, which are patterns of interconnections that occur in a given network at a frequency higher than expected in a random network. In this work we are interested in searching and inferring network motifs in a class of biological networks that can be represented by vertex-colored graphs. We show the computational complexity for many problems related to colorful topological motifs and present efficient algorithms for special cases. A colorful motif can be represented by a graph in which each vertex has a different color. We also present a probabilistic strategy to detect highly frequent motifs in vertex-colored graphs. Experiments on real data sets show that our algorithms are very competitive both in efficiency and in quality of the solutions.

Keywords: Computational biology; Biological networks; Colorful topological motifs; Frequent motifs in graphs (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10878-020-00590-4

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