用神经网络引导图匹配，大幅减少搜索步骤。
Neural Graph Navigation for Intelligent Subgraph Matching
- 将传统暴力枚举改为神经导航搜索，智能跳过无效路径。
- 在六大数据集上，首次匹配步数减少最高达98.2%。
- 适合需要高效图匹配的科研与工业场景。
子图匹配是化学系统到社交网络分析等领域的核心关系模式检测任务，但因搜索空间急剧增长而面临巨大计算挑战。现有方法采用过滤-排序-枚举框架，其中枚举阶段递归匹配查询图与数据图的候选子图。然而，缺乏对子图结构模式的感知导致暴力枚举代价高昂，亟需智能化导航。为此，我们提出神经图导航（NeuGN），一种将暴力枚举转化为神经引导搜索的神经启发式框架。通过在核心枚举过程中融入神经导航机制，在保持基于启发式的完备性保证的同时，相比最先进方法，在六个真实世界数据集上将首次匹配步数最多减少98.2%。
原文摘要 · Abstract (English)
Subgraph matching, a cornerstone of relational pattern detection in domains ranging from biochemical systems to social network analysis, faces significant computational challenges due to the dramatically growing search space. Existing methods address this problem within a filtering-ordering-enumeration framework, in which the enumeration stage recursively matches the query graph against the candidate subgraphs of the data graph. However, the lack of awareness of subgraph structural patterns leads to a costly brute-force enumeration, thereby critically motivating the need for intelligent navigation in subgraph matching. To address this challenge, we propose Neural Graph Navigation (NeuGN), a neuro-heuristic framework that transforms brute-force enumeration into neural-guided search by integrating neural navigation mechanisms into the core enumeration process. By preserving heuristic-based completeness guarantees while incorporating neural intelligence, NeuGN significantly reduces the \textit{First Match Steps} by up to 98.2\% compared to state-of-the-art methods across six real-world datasets.
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