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

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ID: 32631
2018
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Ranked #55 of 56 articles by views in IEEE Transactions on Neural Networks and Learning Systems

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Abstract
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.
Reference Key
yang2018anieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yang, Xu;Qiao, Hong;Liu, Zhi-Yong;
Journal IEEE Transactions on Neural Networks and Learning Systems
Year 2018
DOI
10.1109/TNNLS.2017.2712794
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Keywords Keywords not found

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