Results 11 to 20 of about 75,062 (184)
A Secure Multi-Party Computation Protocol for Graph Editing Distance against Malicious Attacks
Mathematics, 2023 The secure computation of the graph structure is an important element in the field of secure calculation of graphs, which is important in querying data in graphs, since there are no algorithms for the graph edit distance problem that can resist attacks ...
Xin Liu +6 more
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Similar Supergraph Search Based on Graph Edit Distance
Algorithms, 2021 Subgraph and supergraph search methods are promising techniques for the development of new drugs. For example, the chemical structure of favipiravir—an antiviral treatment for influenza—resembles the structure of some components of RNA.
Masataka Yamada, Akihiro Inokuchi
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Edit distance measure for graphs [PDF]
Czechoslovak Mathematical Journal, 2015 The edit number \(s(G,F)\) of two graphs \(G,F\) of order \(n\) is the minimum number of edges needed to be added/deleted from the graph \(G\) to obtain a graph isomorphic to \(F\). In this paper, the author provides values and bounds for \(g(n,l)\), the maximum number \(k\) for which there are \(l\) graphs of order \(n\), each two having edit distance
Dzido, Tomasz, Krzywdziński, Krzysztof
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Graph Edit Distance Reward: Learning to Edit Scene Graph [PDF]
, 2020 14 pages, 6 figures, ECCV camera ready ...
Chen, Lichang +3 more
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The Reeb Graph Edit Distance is Universal [PDF]
Foundations of Computational Mathematics, 2020 AbstractWe consider the setting of Reeb graphs of piecewise linear functions and study distances between them that are stable, meaning that functions which are similar in the supremum norm ought to have similar Reeb graphs. We define an edit distance for Reeb graphs and prove that it is stable and universal, meaning that it provides an upper bound to ...
Bauer, Ulrich +2 more
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Neural Graph Similarity Computation with Contrastive Learning
Applied Sciences, 2022 Computing the similarity between graphs is a longstanding and challenging problem with many real-world applications. Recent years have witnessed a rapid increase in neural-network-based methods, which project graphs into embedding space and devise end-to-
Shengze Hu +3 more
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Accel-Align: a fast sequence mapper and aligner based on the seed–embed–extend method
BMC Bioinformatics, 2021 Background Improvements in sequencing technology continue to drive sequencing cost towards $100 per genome. However, mapping sequenced data to a reference genome remains a computationally-intensive task due to the dependence on edit distance for dealing ...
Yiqing Yan +2 more
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Application of Genetic Algorithms for Finding Edit Distance between Process Models
Моделирование и анализ информационных систем, 2018 Finding graph-edit distance (graph similarity) is an important task in many computer science areas, such as image analysis, machine learning, chemicalinformatics.
Anna A. Kalenkova, Danil A. Kolesnikov
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Co-Attention Graph Pooling for Efficient Pairwise Graph Interaction Learning
IEEE Access, 2023 Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data. However, previous works mainly focused on understanding single graph inputs while many real-world applications require pair-wise analysis for ...
Junhyun Lee +3 more
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Map Edit Distance vs. Graph Edit Distance for Matching Images [PDF]
, 2013 Generalized maps are widely used to model the topology of nD objects (such as 2D or 3D images) by means of incidence and adjacency relationships between cells (0D vertices, 1D edges, 2D faces, 3D volumes, ...). Recently, we have introduced a map edit distance.
Combier, Camille +2 more
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