Subgraph matching is one fundamental issue in the research area of graph analysis, which has a wild range of applications, including question answering, semantic search and community detection. Recent studies have designed some near-optimal matching orders and data indexes to reduce the unpromising redundant calculations. However, the influences of recalculation on some positive candidate vertices were ignored in the iterative process of subgraph matching. In this paper, the novel concepts of an anchored node pair and anchored relationship are proposed as the theoretical basis to solve recalculation on subgraph matching. Thus, two key aspects are considered to reduce the redundant and recalculated node pairs. The first aspect involves using the dominating relationship of the anchored node and its follower to prune the negative node pairs, and an index of matching-driven flow graph is built to minimize the positive candidate vertices by using a heuristic algorithm. The second aspect involves exploring the anchored relationship to analyze the recalculated region of a matching stream, and two novel strategies are designed to manipulate the intermediate results of the partial subgraph isomorphism to avoid revalidation in the subgraph matching process. Extensive empirical studies on real and synthetic datasets demonstrate that our techniques outperform the state-of-the-art algorithms.

子图匹配是图分析研究领域的一个基本问题，它具有广泛的应用，包括问答、语义搜索和社区检测。最近的研究设计了一些接近最优的匹配顺序和数据索引，以减少毫无希望的冗余计算。然而，在子图匹配的迭代过程中忽略了重新计算对一些正候选顶点的影响。在本文中，提出了锚节点对和锚关系的新概念，作为解决子图匹配重新计算的理论基础。因此，考虑两个关键方面来减少冗余和重新计算的节点对。第一个方面涉及使用锚定节点及其跟随者的支配关系来修剪负节点对，并利用启发式算法建立匹配驱动流图的索引以最小化正候选顶点。第二个方面涉及探索锚定关系以分析匹配流的重新计算区域，并设计了两种新颖的策略来操纵部分子图同构的中间结果，以避免子图匹配过程中的重新验证。对真实和合成数据集的大量实证研究表明，我们的技术优于最先进的算法。并且设计了两种新颖的策略来操纵部分子图同构的中间结果，以避免在子图匹配过程中重新验证。对真实和合成数据集的大量实证研究表明，我们的技术优于最先进的算法。并且设计了两种新颖的策略来操纵部分子图同构的中间结果，以避免在子图匹配过程中重新验证。对真实和合成数据集的大量实证研究表明，我们的技术优于最先进的算法。