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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Book ChapterDOI
A Deep Neural Network Architecture to Estimate Node Assignment Costs for the Graph Edit Distance
TL;DR: The aim of this paper is to present a model to compute the assignments costs for the Graph Edit Distance by means of a Deep Neural Network previously trained with a set of pairs of graphs properly matched, and empirically show a major improvement using the method.
Book ChapterDOI
Complexity of computing distances between geometric trees
TL;DR: The NP completeness of tree edit distance and another natural distance measure, QED, for geometric trees with vector valued edge attributes is proved.
Proceedings ArticleDOI
H2MN: Graph Similarity Learning with Hierarchical Hypergraph Matching Networks
TL;DR: Wang et al. as mentioned in this paper proposed Hierarchical Hypergraph Matching Networks (H2sup>MN), which takes each hyperedge as a subgraph to perform subgraph matching, which could capture the rich substructure similarities across the graph.
Book ChapterDOI
Efficient Suboptimal Graph Isomorphism
TL;DR: A novel approach for the efficient computation of graph isomorphism is presented, based on bipartite graph matching by means of Munkres' algorithm, and it is shown that the proposed algorithm rejects only very few pairs of graphs.
Posted Content
Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity.
TL;DR: This work introduces UGRAPHEMB ( Unsupervised Graph-level Embbedding) for learning graph-level representations in an unsupervised and inductive way.
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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