Many scientific applications entail solving the subgraph isomorphism problem, i.e., given an input pattern graph, find all the subgraphs of a (usually much larger) target graph that are structurally equivalent to that input. Because subgraph isomorphism is NP-complete, methods to solve it have to use heuristics. This work evaluates subgraph isomorphism methods to assess their computational behavior on a wide range of synthetic and real graphs. Surprisingly, our experiments show that, among the leading algorithms, certain heuristics based only on pattern graphs are the most efficient.

中文翻译：

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基于仅模式启发式的快速子图匹配策略。

许多科学应用需要解决子图同构问题，即，给定输入模式图，找到结构上与该输入等效的（通常更大）目标图的所有子图。因为子图同构是NP完全的，所以解决它的方法必须使用启发式。这项工作评估了子图同构方法，以评估它们在各种合成图和实图上的计算性能。令人惊讶的是，我们的实验表明，在领先的算法中，仅基于模式图的某些启发式方法是最有效的。