论文标题
高阶关系结构和图形匹配的采矿
High-Order Relation Construction and Mining for Graph Matching
论文作者
论文摘要
图对匹配对跨两个或多个图的相应节点。问题很难，因为很难捕获跨图的结构相似性，尤其是在大图上。我们建议将高阶信息合并以匹配大规模图。首次介绍了迭代的线图来描述这样的高阶信息，基于我们提出一种新的图形匹配方法，称为高阶图匹配网络（HGMN），不仅要学习局部结构对应关系，而且还学习跨图的超边关系。从理论上讲，我们证明迭代的线图在对齐节点方面比图形卷积网络更具表现力。通过施加实用的约束，HGMN可扩展到大规模图。在各种设置上的实验结果表明，HGMN比最先进的方法获得更准确的匹配结果，从而有效地验证了我们的方法可有效捕获不同图形之间的结构相似性。
Graph matching pairs corresponding nodes across two or more graphs. The problem is difficult as it is hard to capture the structural similarity across graphs, especially on large graphs. We propose to incorporate high-order information for matching large-scale graphs. Iterated line graphs are introduced for the first time to describe such high-order information, based on which we present a new graph matching method, called High-order Graph Matching Network (HGMN), to learn not only the local structural correspondence, but also the hyperedge relations across graphs. We theoretically prove that iterated line graphs are more expressive than graph convolution networks in terms of aligning nodes. By imposing practical constraints, HGMN is made scalable to large-scale graphs. Experimental results on a variety of settings have shown that, HGMN acquires more accurate matching results than the state-of-the-art, verifying our method effectively captures the structural similarity across different graphs.
