Abstract
The subgraph isomorphism (SubGI) problem is known to be a NP-Complete problem. Several methodologies use heuristic approaches to solve it, differing into the strategy to search the occurrences of a graph into another. This choice strongly influences their computational effort requirement. We investigate seven search strategies where global and local topological properties of the graphs are exploited by means of weighted graph centrality measures. Results on benchmarks of biological networks show the competitiveness of the proposed seven alternatives and that, among them, local strategies predominate on sparse target graphs, and closeness- and eigenvector-based strategies outperform on dense graphs.
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Acknowledgments
This work has been partially supported by the following projects: GNCS-INDAM, Fondo Sociale Europeo, and National Research Council Flagship Projects Interomics. This work has been partially supported by the project of the Italian Ministry of education, Universities and Research (MIUR) “Dipartimenti di Eccellenza 2018–2022”.
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Bonnici, V., Caligola, S., Aparo, A., Giugno, R. (2020). Centrality Speeds the Subgraph Isomorphism Search Up in Target Aware Contexts. In: Raposo, M., Ribeiro, P., Sério, S., Staiano, A., Ciaramella, A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science(), vol 11925. Springer, Cham. https://doi.org/10.1007/978-3-030-34585-3_3
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DOI: https://doi.org/10.1007/978-3-030-34585-3_3
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