In this paper, we study the problem of graph similarity search with graph edit distance (GED) constraints. Due to the NP-hardness of GED computation, existing solutions to this problem adopt the filtering-and-verification framework with a main focus on the filtering phase to generate a small number of candidate graphs. However, they have a limitation that the number of candidates grows extremely rapidly as a GED threshold increases. To address the limitation, we propose a new approach that utilizes GED computation results in generating candidate graphs. The main idea is that whenever we identify a result graph of the query, we immediately regenerate candidate graphs using a subset of pre-computed graphs similar to the identified result graph. To speed up GED computation, we also develop a novel GED computation algorithm. The proposed algorithm reduces the search space for GED computation by utilizing a series of filtering techniques, which have been used to generate candidates in existing solutions. Experimental results on real datasets demonstrate the proposed approach significantly outperforms the state-of-the art techniques.

中文翻译：

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Nass：图相似性搜索的新方法

在本文中，我们研究了具有图编辑距离（GED）约束的图相似性搜索问题。由于 GED 计算的 NP 难度，该问题的现有解决方案采用过滤和验证框架，主要关注过滤阶段以生成少量候选图。然而，他们有一个限制，即随着 GED 阈值的增加，候选人的数量增长得非常快。为了解决这个限制，我们提出了一种利用 GED 计算结果生成候选图的新方法。主要思想是，每当我们识别查询的结果图时，我们立即使用类似于识别出的结果图的预先计算图的子集重新生成候选图。为了加速 GED 计算，我们还开发了一种新颖的 GED 计算算法。所提出的算法通过利用一系列过滤技术来减少 GED 计算的搜索空间，这些技术已被用于在现有解决方案中生成候选。在真实数据集上的实验结果表明，所提出的方法明显优于最先进的技术。