Finding vertex-to-vertex correspondences in real-world graphs is a challenging task with applications in a wide variety of domains. Structural matching based on graphs connectivities has attracted considerable attention, while the integration of all the other information stemming from vertices and edges attributes has been mostly left aside. Here we present the Graph Attributes and Structure Matching (GASM) algorithm, which provides high-quality solutions by integrating all the available information in a unified framework. Parameters quantifying the reliability of the attributes can tune how much the solutions should rely on the structure or on the attributes. We further show that even without attributes GASM consistently finds as-good-as or better solutions than state-of-the-art algorithms, with similar processing times.

翻译：在现实世界图中寻找顶点到顶点的对应关系是一项具有挑战性的任务，在众多领域均有广泛应用。基于图连通性的结构匹配已受到广泛关注，而源自顶点与边属性的其他信息整合则大多被忽视。本文提出图属性与结构匹配（GASM）算法，该算法通过将所有可用信息整合至统一框架中提供高质量解决方案。量化属性可靠性的参数可调节解决方案对结构或属性的依赖程度。我们进一步证明，即使在没有属性的情况下，GASM仍能以相近的处理时间，持续获得与最先进算法相当或更优的解决方案。