Exact Computation of Graph Edit Distance for Uniform and Non-uniform Metric Edit Costs

2017 ◽  
pp. 211-221 ◽  
Author(s):  
David B. Blumenthal ◽  
Johann Gamper
Keyword(s):  
Edit Distance ◽  
Graph Edit Distance ◽  
Exact Computation

On the exact computation of the graph edit distance

2020 ◽  
Vol 134 ◽  
pp. 46-57 ◽  
Author(s):  
David B. Blumenthal ◽  
Johann Gamper
Keyword(s):  
Edit Distance ◽  
Graph Edit Distance ◽  
Exact Computation

On-line learning the graph edit distance costs

2021 ◽  
Author(s):  
Elena Rica ◽  
Susana Álvarez ◽  
Francesc Serratosa
Keyword(s):  
Edit Distance ◽  
Graph Edit Distance ◽  
On Line ◽  
On Line Learning

scholarly journals An efficient algorithm for graph edit distance computation

2019 ◽  
Vol 163 ◽  
pp. 762-775 ◽  
Author(s):  
Xiaoyang Chen ◽  
Hongwei Huo ◽  
Jun Huan ◽  
Jeffrey Scott Vitter
Keyword(s):  
Efficient Algorithm ◽  
Edit Distance ◽  
Graph Edit Distance ◽  
Distance Computation

scholarly journals Redefining the Graph Edit Distance

2021 ◽  
Vol 2 (6) ◽  
Author(s):  
Francesc Serratosa
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Triangle Inequality ◽  
Edit Distance ◽  
Minimum Amount ◽  
Graph Edit Distance ◽  
Edit Operation ◽  
Attributed Graphs ◽  
Distance Properties

AbstractGraph edit distance has been used since 1983 to compare objects in machine learning when these objects are represented by attributed graphs instead of vectors. In these cases, the graph edit distance is usually applied to deduce a distance between attributed graphs. This distance is defined as the minimum amount of edit operations (deletion, insertion and substitution of nodes and edges) needed to transform a graph into another. Since now, it has been stated that the distance properties have to be applied [(1) non-negativity (2) symmetry (3) identity and (4) triangle inequality] to the involved edit operations in the process of computing the graph edit distance to make the graph edit distance a metric. In this paper, we show that there is no need to impose the triangle inequality in each edit operation. This is an important finding since in pattern recognition applications, the classification ratio usually maximizes in the edit operation combinations (deletion, insertion and substitution of nodes and edges) that the triangle inequality is not fulfilled.

scholarly journals Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening

2020 ◽  
Vol 20 (18) ◽  
pp. 1582-1592 ◽  
Author(s):  
Carlos Garcia-Hernandez ◽  
Alberto Fernández ◽  
Francesc Serratosa
Keyword(s):  
Virtual Screening ◽  
Edit Distance ◽  
Graph Matching ◽  
Optimization Techniques ◽  
Graph Edit Distance ◽  
Structure Activity ◽  
Future Drug ◽  
Minimum Number ◽  
Type Node ◽  
Activity Information

Background: Graph edit distance is a methodology used to solve error-tolerant graph matching. This methodology estimates a distance between two graphs by determining the minimum number of modifications required to transform one graph into the other. These modifications, known as edit operations, have an edit cost associated that has to be determined depending on the problem. Objective: This study focuses on the use of optimization techniques in order to learn the edit costs used when comparing graphs by means of the graph edit distance. Methods: Graphs represent reduced structural representations of molecules using pharmacophore-type node descriptions to encode the relevant molecular properties. This reduction technique is known as extended reduced graphs. The screening and statistical tools available on the ligand-based virtual screening benchmarking platform and the RDKit were used. Results: In the experiments, the graph edit distance using learned costs performed better or equally good than using predefined costs. This is exemplified with six publicly available datasets: DUD-E, MUV, GLL&GDD, CAPST, NRLiSt BDB, and ULS-UDS. Conclusion: This study shows that the graph edit distance along with learned edit costs is useful to identify bioactivity similarities in a structurally diverse group of molecules. Furthermore, the target-specific edit costs might provide useful structure-activity information for future drug-design efforts.

scholarly journals An Edge-Based Approach for Virtual Network Embedding Based on the Graph Edit Distance

2021 ◽  
Author(s):  
Ze Xi Xu ◽  
Lei Zhuang ◽  
Meng Yang He ◽  
Si Jin Yang ◽  
Yu Song ◽  
...  
Keyword(s):  
Edit Distance ◽  
Virtual Network ◽  
Cost Ratio ◽  
Virtual Network Embedding ◽  
Network Embedding ◽  
Graph Edit Distance ◽  
Network Resources ◽  
Network Resource ◽  
Distance Method ◽  
Edge Based

Abstract Virtualization and resource isolation techniques have enabled the efficient sharing of networked resources. How to control network resource allocation accurately and flexibly has gradually become a research hotspot due to the growth in user demands. Therefore, this paper presents a new edge-based virtual network embedding approach to studying this problem that employs a graph edit distance method to accurately control resource usage. In particular, to manage network resources efficiently, we restrict the use conditions of network resources and restrict the structure based on common substructure isomorphism and an improved spider monkey optimization algorithm is employed to prune redundant information from the substrate network. Experimental results showed that the proposed method achieves better performance than existing algorithms in terms of resource management capacity, including energy savings and the revenue-cost ratio.

scholarly journals Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

2018 ◽  
pp. 470-480 ◽  
Author(s):  
Paul Maergner ◽  
Vinaychandran Pondenkandath ◽  
Michele Alberti ◽  
Marcus Liwicki ◽  
Kaspar Riesen ◽  
...  
Keyword(s):  
Edit Distance ◽  
Signature Verification ◽  
Graph Edit Distance ◽  
Offline Signature Verification
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