CEMR: 一种具有冗余扩展消除的有效子图匹配算法
CEMR: An Effective Subgraph Matching Algorithm with Redundant Extension Elimination
AI总结：
CEMR通过共同扩展合并和重用技术以及剪枝策略，有效减少冗余计算，提升子图匹配效率。
摘要：
子图匹配是图分析中的基本问题，具有广泛的应用。然而，由于其固有的NP难性，在大型现实图上高效地枚举子图匹配仍然极具挑战性。大多数现有工作采用深度优先搜索（DFS）回溯策略，其中部分嵌入沿着搜索树的分支逐步扩展，直到找到完整嵌入或无法进一步扩展为止。这种范式的重大限制是枚举过程中发生的大量重复计算，这会增加整体运行时间。为克服这一限制，我们提出了一种新的子图匹配算法CEMR。它结合了两种技术来减少重复扩展：共同扩展合并，它利用黑白顶点编码；共同扩展重用，它采用共同扩展缓冲区。此外，我们设计了两种剪枝技术以丢弃不值得的搜索分支。在现实数据集和多样化的查询工作负载上的广泛实验表明，CEMR优于最先进的子图匹配方法。
英文摘要：
Subgraph matching is a fundamental problem in graph analysis with a wide range of applications. However, due to its inherent NP-hardness, enumerating subgraph matches efficiently on large real-world graphs remains highly challenging. Most existing works adopt a depth-first search (DFS) backtracking strategy, where a partial embedding is gradually extended in a DFS manner along a branch of the search trees until either a full embedding is found or no further extension is possible. A major limitation of this paradigm is the significant amount of duplicate computation that occurs during enumeration, which increases the overall runtime. To overcome this limitation, we propose a novel subgraph matching algorithm, CEMR. It incorporates two techniques to reduce duplicate extensions: common extension merging, which leverages a black-white vertex encoding, and common extension reusing, which employs common extension buffers. In addition, we design two pruning techniques to discard unpromising search branches. Extensive experiments on real-world datasets and diverse query workloads demonstrate that CEMR outperforms state-of-the-art subgraph 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。
