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Searching and inferring colorful topological motifs in vertex-colored graphs

Author

Listed:
  • 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)

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.

Suggested Citation

  • Diego P. Rubert & Eloi Araujo & Marco A. Stefanes & Jens Stoye & Fábio V. Martinez, 2020. "Searching and inferring colorful topological motifs in vertex-colored graphs," Journal of Combinatorial Optimization, Springer, vol. 40(2), pages 379-411, August.
    • Handle: RePEc:spr:jcomop:v:40:y:2020:i:2:d:10.1007_s10878-020-00590-4
    DOI: 10.1007/s10878-020-00590-4
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