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.
CITATION STYLE
Bonnici, V., Caligola, S., Aparo, A., & Giugno, R. (2020). Centrality Speeds the Subgraph Isomorphism Search Up in Target Aware Contexts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11925 LNBI, pp. 19–26). Springer. https://doi.org/10.1007/978-3-030-34585-3_3
Mendeley helps you to discover research relevant for your work.