A fast labeled graph matching algorithm based on edge matching and guided by search route

Yintang Dai, Shihan Zhang
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引用次数: 2

Abstract

This paper presents a fast labeled graph matching algorithm called Graph Explorer (GE) algorithm, which can be categorized into the tree search based graph matching (TSGM) algorithms of exact graph/subgraph matching. Not like the other node-centric TSGM algorithms, the GE algorithm focuses on edges matching. It constructs search state of partially matched subgraph by edge and edge. It converts graph matching problem into a path search problem in the space of search states. Under the guidance of the search path, it avoided repeated label checking by inheriting state tree structure for caching and fast visiting matched nodes and edge. By a carefully optimized search route and intelligent backtracking, GE algorithm avoided a large amount of the invalid search states and improved performance to be almost linear to the number of edges of pattern graph with low ambiguity. While traditional TSGM are suffering the call stack overflow problem caused by recursive function calls, it overcame this problem by a dynamic state queue. It can handle extra large size of pattern (up to 10,000 nodes). The experiment shows the performance of GE is better than similar algorithms and it is more resistant to ambiguities.
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一种基于边缘匹配和搜索路径引导的快速标记图匹配算法
本文提出了一种快速标记图匹配算法——图资源管理器(GE)算法,该算法可分为基于树搜索的精确图/子图匹配算法(TSGM)。与其他以节点为中心的TSGM算法不同,GE算法侧重于边缘匹配。它通过边和边构造部分匹配子图的搜索状态。它将图匹配问题转化为搜索状态空间中的路径搜索问题。在搜索路径的指导下,通过继承状态树结构进行缓存,快速访问匹配的节点和边,避免了重复的标签检查。GE算法通过精心优化搜索路径和智能回溯,避免了大量无效搜索状态,提高了性能,使其与模式图的边数几乎呈线性关系,模糊度低。传统TSGM存在递归函数调用导致的调用堆栈溢出问题,而TSGM通过动态状态队列克服了这一问题。它可以处理超大规模的模式(多达10,000个节点)。实验结果表明,该算法的性能优于同类算法,并且具有更好的抗歧义性。
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