Graph matching is the task of finding the similarity between the two graphs. Error-tolerant graph matching is the process of computing the similarity between the two graphs, where some flexibility to noise or error is allowed to approximate the value of graph matching. In this paper, we present a framework for graph matching by utilizing the centrality measures to ignore the least central nodes of the graphs. Experimental evaluation shows that this approach can be useful to reduce the overall matching time and it can provide time versus accuracy trade-off.

中文翻译：

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利用节点中心性信息的容错近似图匹配

图匹配是发现两个图之间相似性的任务。容错图匹配是计算两个图之间相似度的过程，其中允许一定程度的噪声或误差灵活性，以近似图匹配的值。在本文中，我们提出了一种通过利用中心性度量来忽略图的最小中心节点的图匹配框架。实验评估表明，该方法可用于减少总体匹配时间，并且可以提供时间与准确性之间的权衡。