Learning Exact Graph Edit Distance

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.

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

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200603122000 ◽

2020 ◽

Vol 20 (18) ◽

pp. 1582-1592 ◽

Cited By ~ 1

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.

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

10.21203/rs.3.rs-1029589/v1 ◽

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.

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Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

Lecture Notes in Computer Science - Structural, Syntactic, and Statistical Pattern Recognition ◽

10.1007/978-3-319-97785-0_45 ◽

2018 ◽

pp. 470-480 ◽

Cited By ~ 2

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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Efficient Approximate Approach for Graph Edit Distance Problem

Pattern Recognition Letters ◽

10.1016/j.patrec.2021.08.027 ◽

2021 ◽

Author(s):

Adel Dabah ◽

Ibrahim Chegrane ◽

Saïd Yahiaoui

Keyword(s):

Edit Distance ◽

Graph Edit Distance ◽

Approximate Approach ◽

Distance Problem

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Context-Aware Presentation of Linked Data on Mobile

Information Retrieval and Management ◽

10.4018/978-1-5225-5191-1.ch087 ◽

2018 ◽

pp. 1940-1971

Author(s):

Luca Costabello ◽

Fabien Gandon

Keyword(s):

Linked Data ◽

Edit Distance ◽

Research Question ◽

Context Aware ◽

Graph Edit Distance ◽

Optimal Error ◽

Presentation Level ◽

Rdf Graphs ◽

Mobile Context ◽

Proper Context

In this paper the authors focus on context-aware adaptation for linked data on mobile. They split up the problem in two sub-questions: how to declaratively describe context at RDF presentation level, and how to overcome context imprecisions and incompleteness when selecting the proper context description at runtime. The authors answer their two-fold research question with PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.

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