从一个到所有：学习匹配异构和部分重叠的图
From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs
摘要：
图匹配是人工智能应用的核心，其目的是寻找两个图中节点的对应关系。然而，在现实世界的应用中，匹配异构图与部分重叠仍然是一个具有挑战性的问题。本文提出了第一个实用的学习匹配的方法，以满足这一挑战。建议的无监督方法采用了一种新的部分OT范式，同时学习运输计划和节点嵌入。在从一个到所有的方式，整个学习过程被分解成一系列易于解决的子程序，其中每个只处理一种类型的节点的对齐。还提出了一种用于搜索传输质量的机制。实验结果表明，该方法优于国家的最先进的图匹配方法。
英文摘要：
Recent years have witnessed a flurry of research activity in graph matching, which aims at finding the correspondence of nodes across two graphs and lies at the heart of many artificial intelligence applications. However, matching heterogeneous graphs with partial overlap remains a challenging problem in real-world applications. This paper proposes the first practical learning-to-match method to meet this challenge. The proposed unsupervised method adopts a novel partial OT paradigm to learn a transport plan and node embeddings simultaneously. In a from-one-to-all manner, the entire learning procedure is decomposed into a series of easy-to-solve sub-procedures, each of which only handles the alignment of a single type of nodes. A mechanism for searching the transport mass is also proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art graph matching methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
