Journal ArticleDOI
Approximate graph edit distance computation by means of bipartite graph matching
Kaspar Riesen,Horst Bunke +1 more
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TLDR
A novel algorithm is introduced which allows us to approximately, or suboptimally, compute edit distance in a substantially faster way and is emprically verified that the accuracy of the suboptimal distance remains sufficiently accurate for various pattern recognition applications.About:
This article is published in Image and Vision Computing.The article was published on 2009-06-01. It has received 654 citations till now. The article focuses on the topics: Graph operations & Line graph.read more
Citations
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Journal ArticleDOI
A novel edge-centric approach for graph edit similarity computation
TL;DR: This paper presents CSI_GED, a novel edge-centric approach for computing graph edit distance through common sub-structure isomorphisms enumeration, which outperforms the state-of-the-art indexing-based methods by over three orders of magnitude.
Book ChapterDOI
Improving Hausdorff Edit Distance Using Structural Node Context
TL;DR: In an experimental evaluation on diverse graph data sets, it is demonstrated that the proposed generalization of Hausdorff edit distance can significantly improve the accuracy of graph classification while maintaining low computational complexity.
Proceedings ArticleDOI
Generic object recognition by graph structural expression
TL;DR: In the proposed method, the graph is constructed by connecting SIFT keypoints with lines, and then structural representation with location information is achieved.
Book ChapterDOI
Feature Ranking Algorithms for Improving Classification of Vector Space Embedded Graphs
Kaspar Riesen,Horst Bunke +1 more
TL;DR: This paper takes a more fundamental approach and regard the problem of prototype selection as a feature selection problem, for which many methods are available and shows the feasibility of graph embedding based on prototypes obtained from feature selection algorithms.
Posted Content
Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
TL;DR: This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver.
References
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Journal ArticleDOI
The Protein Data Bank
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
Journal ArticleDOI
The Hungarian method for the assignment problem
TL;DR: This paper has always been one of my favorite children, combining as it does elements of the duality of linear programming and combinatorial tools from graph theory, and it may be of some interest to tell the story of its origin this article.
Journal ArticleDOI
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
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