A Graph Database Repository and Performance Evaluation Metrics for Graph Edit Distance

Abu-Aisheh, Zeina; Raveaux, Romain; Ramel, Jean-Yves

doi:10.1007/978-3-319-18224-7_14

Cited by 12 publications

(17 citation statements)

References 16 publications

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“…Note that the GED-estimation method of (Gaüzère et al, 2016) needs the data to be integer typed, hence it could not be applied to the CMU datasets in our experiments. The graph pairs we tested on this dataset are the same as in (Abu-Aisheh et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

Approximate Graph Edit Distance by Several Local Searches in Parallel

Daller

Bougleux

Gaüzère

et al. 2018

Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods

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Section: Methodsmentioning

confidence: 99%

Approximate Graph Edit Distance by Several Local Searches in Parallel

Daller

Bougleux

Gaüzère

et al. 2018

Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods

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“…Since the graphs for which GED based methods are applied are mostly undirected [2,50,62], most heuristics for GED are presented for undirected labeled graphs, although they can usually be easily generalized to directed graphs. For the ease of presentation, we restrict to undirected graphs also in this paper.…”

Section: Preliminariesmentioning

confidence: 99%

Comparing heuristics for graph edit distance computation

Blumenthal¹,

Boria²,

Gamper³

et al. 2019

The VLDB Journal

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Because of its flexibility, intuitiveness, and expressivity, the graph edit distance (GED) is one of the most widely used distance measures for labeled graphs. Since exactly computing GED is NP-hard, over the past years, various heuristics have been proposed. They use techniques such as transformations to the linear sum assignment problem with error-correction, local search, and linear programming to approximate GED via upper or lower bounds. In this paper, we provide a systematic overview of the most important heuristics. Moreover, we empirically evaluate all compared heuristics within an integrated implementation.

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“…To this end, two databases MUTA [Abu-Aisheh et al, 2015] and PAH [Brun, 2016], out of many, are selected from the public datasets [Abu-Aisheh et al, 2015, Brun, 2016, Moreno-García et al, 2016. Both databases consist of graphs representing chemical molecules.…”

Section: Accuracy Of the Heuristics Wrt Optimal Solutionsmentioning

confidence: 99%

“…ces, ced, cei). Based on [Abu-Aisheh et al, 2015], the cost values for MUTA are: cvs = 5500, cvd = cvi = 5500, ces = 0, ced = cei = 825. Note that cvs becomes 0 when two vertices have the same attribute values.…”

Section: Accuracy Of the Heuristics Wrt Optimal Solutionsmentioning

confidence: 99%

“…It turns out that the first one is harder to solve that the second, because the second considers the attributes, which helps assigning the vertices and edges. Regarding the cost values as defined by [Abu-Aisheh et al, 2015], for HOUSE-NA: cvs = 0, cvd = cvi = ∞, ces = 0, ced = cei = 0.5; HOUSE-A: cvs = 0.5 × |µ(u) − µ (v)|, cvd = cvi = ∞, ces = 0, ced = cei = 0.5, with u ∈ V and v ∈ V . Finally, the heuristics parameters are set as follows.…”

Section: Accuracy Of the Heuristics Wrt Ground-truth Matchingmentioning

confidence: 99%

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Graph edit distance: Accuracy of local branching from an application point of view

Darwiche

Conte

Raveaux

et al. 2020

Pattern Recognition Letters

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In the context of graph-based representations, comparing and measuring the dissimilarities between graphs can be done by solving the Graph Edit Distance (GED) problem. It is well known and embedded in many application fields such as Computer Vision and Cheminformatics. GED is a NP-hard minimization problem, therefore the optimal solution cannot be found in reasonable time. The GED problem has been addressed by exact approaches like Mixed Integer Linear Programs (MILP) formulations and heuristic approaches like beam-search, bipartite graph matching among others. Recently, a novel heuristic, called local branching (LocBra) for the GED problem, has been proposed and shown to be efficient. In this work, the focus is on evaluating LocBra with other competitive heuristics available in the literature from an application point of view. Moreover, it tries to answer the following question: is it important to compute an accurate GED regarding the final applications? Similarity search and graph matching are considered as final applications. Three experiments are conducted to evaluate the accuracy and efficiency of the heuristics. The quality of the obtained solutions and matching w.r.t. optimal solutions and ground-truth matching is studied. The results of those experiments show that LocBra has a high correlation with the optimal solutions and the ground-truth matching.

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A Graph Database Repository and Performance Evaluation Metrics for Graph Edit Distance

Cited by 12 publications

References 16 publications

Approximate Graph Edit Distance by Several Local Searches in Parallel

Approximate Graph Edit Distance by Several Local Searches in Parallel

Comparing heuristics for graph edit distance computation

Graph edit distance: Accuracy of local branching from an application point of view

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