An Algorithm for Finding the Most Similar Given Sized Subgraphs in Two Weighted Graphs

被引：10

作者：

Yang, Xu ^{[1
]}

Qiao, Hong ^{[1
,2
,3
]}

Liu, Zhi-Yong ^{[1
,2
,3
]}

机构：

[1] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China

[2] Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China

[3] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2018年 / 29卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Adjacency matrix; graph algorithms; graph matching; weighted common subgraph (WCS) matching;

D O I：

10.1109/TNNLS.2017.2712794

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a weighted common subgraph (WCS) matching algorithm to find the most similar subgraphs in two labeled weighted graphs. WCS matching, as a natural generalization of equal-sized graph matching and subgraph matching, has found wide applications in many computer vision and machine learning tasks. In this brief, WCS matching is first formulated as a combinatorial optimization problem over the set of partial permutation matrices. Then, it is approximately solved by a recently proposed combinatorial optimization framework-graduated nonconvexity and concavity procedure. Experimental comparisons on both synthetic graphs and real-world images validate its robustness against noise level, problem size, outlier number, and edge density.

引用

页码：3295 / 3300

页数：6