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Charting the Normal Development of Structural Brain Connectivity in Utero using Diffusion MRI
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A survey of graph edit distance
Pattern Analysis and Applications, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gao, Xinbo +3 more
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Computing Graph Edit Distance via Neural Graph Matching
Proceedings of the VLDB Endowment, 2023Graph edit distance (GED) computation is a fundamental NP-hard problem in graph theory. Given a graph pair ( G 1 , G 2 ), GED is defined as the minimum number of primitive operations converting G 1 to G 2
Chengzhi Piao +5 more
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Approximate Graph Edit Distance in Quadratic Time
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020Graph edit distance is one of the most flexible and general graph matching models available. The major drawback of graph edit distance, however, is its computational complexity that restricts its applicability to graphs of rather small size. Recently, the authors of the present paper introduced a general approximation framework for the graph edit ...
Kaspar, Riesen +2 more
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2015
In pattern recognition and data mining applications, where the underlying data is characterized by complex structural relationships, graphs are often used as a formalism for object representation. Yet, the high representational power and flexibility of graphs is accompanied by a significant increase of the complexity of many algorithms.
Kaspar Riesen +3 more
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In pattern recognition and data mining applications, where the underlying data is characterized by complex structural relationships, graphs are often used as a formalism for object representation. Yet, the high representational power and flexibility of graphs is accompanied by a significant increase of the complexity of many algorithms.
Kaspar Riesen +3 more
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Graph Edit Distance Testing through Synthetic Graphs Generation
2018 24th International Conference on Pattern Recognition (ICPR), 2018Error-tolerant graph matching has been demonstrated to be an NP-problem, for this reason, several suboptimal algorithms have been presented with the aim of making the runtime acceptable in some applications. These algorithms have been tested with relative small graphs due to the computation of the true distance for comparison purposes is too costly. We
Pep Santacruz, Francese Serratosa
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2015
Graph edit distance measures distances between two graphs \(g_1\) and \(g_2\) by the amount of distortion that is needed to transform \(g_1\) into \(g_2\). The basic distortion operations of graph edit distance can cope with arbitrary labels on both nodes and edges as well as with directed or undirected edges.
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Graph edit distance measures distances between two graphs \(g_1\) and \(g_2\) by the amount of distortion that is needed to transform \(g_1\) into \(g_2\). The basic distortion operations of graph edit distance can cope with arbitrary labels on both nodes and edges as well as with directed or undirected edges.
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GTED: Graph Traversal Edit Distance
2018Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algorithm from the underlying geometric space, which renders graph kernels important for the aforementioned ...
Ali Ebrahimpour Boroojeny +5 more
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Iterative Bipartite Graph Edit Distance Approximation
2014 11th IAPR International Workshop on Document Analysis Systems, 2014One of the major tasks in many applications in the field of document analysis is the computation of dissimilarities between two or more objects from a given problem domain. Hence, employing graphs as representation formalism evokes the need for powerful, fast and flexible graph based dissimilarity models.
Kaspar Riesen +2 more
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