Abstract
Subgraph isomorphism is one of the most challenging problems on graph-based representations. Despite many efficient sequential algorithms have been proposed over the last decades, solving this problem on large graphs is still a time demanding task. For this reason, there is a recently growing interest in realizing effective parallel algorithms able to exploit at their best the modern multi-core architectures commonly available on servers and workstations. We propose a comparison of four parallel algorithms derived from the state-of-the-art sequential algorithm VF3-Light; two of them were presented in previous works, while the other two are introduced in this paper. In order to evaluate strong points and weaknesses of each algorithm, we performed a benchmark over six datasets of random large and dense graphs, both labelled and unlabelled, measuring memory usage, speed-up and efficiency. We also add a comparison with a different parallel algorithm, named Glasgow, that is not derived from VF3-Light.
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Carletti, V., Foggia, P., Greco, A., Vento, M. (2021). Parallel Subgraph Isomorphism on Multi-core Architectures: A Comparison of Four Strategies Based on Tree Search. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science(), vol 12644. Springer, Cham. https://doi.org/10.1007/978-3-030-73973-7_24
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