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Journal ArticleDOI

Approximate graph edit distance computation by means of bipartite graph matching

Kaspar Riesen, +1 more
- 01 Jun 2009 - 
- Vol. 27, Iss: 7, pp 950-959
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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.
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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.

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Citations
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Journal ArticleDOI

Efficient approximate approach for graph edit distance problem

TL;DR: In this article, a tree-based approximate approach for the GED problem is proposed. But the problem is NP-hard and the search space increases exponentially making exact approaches impossible to use for large graphs, and there is a huge need for approaches that give nearoptimal results in reasonable time.
Journal ArticleDOI

Convex graph invariant relaxations for graph edit distance

TL;DR: In this paper, a new family of computationally tractable convex relaxations for obtaining lower bounds on graph edit distance is proposed, which can be tailored to the structural properties of the particular graphs via convex graph invariants.
Posted Content

Fast Computation of Graph Edit Distance.

TL;DR: BSS GED is a novel vertex-based mapping method for the GED computation that outperforms the state-of-the-art GED methods and applies to the graph similarity search problem and the practical results confirm its efficiency.
Journal ArticleDOI

Localization of Autonomous Robot in an Urban Area Based on SURF Feature Extraction of Images

TL;DR: The authors believe that the proposed method to localize an autonomous robot in a specific area using the given set of images without external help will add value to other methods using some external tools even when those tools are unavailable.
Proceedings ArticleDOI

LAN: Learning-based Approximate k-Nearest Neighbor Search in Graph Databases

TL;DR: This paper proposes a learning-based k-ANN search method to reduce NDC and proposes a compressed GNN-graph to accelerate the neighbor ranking model and the initial node selection model, and proves that learning efficiency is improved without degrading the accuracy.
References
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Journal ArticleDOI

The Protein Data Bank

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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