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The adaptation rules rely on graph matching and graph rewriting.
It provides the reader with a basic example and simple pattern matching and graph algorithms for solving the sample problem.
Path matching and graph matching to query certain metabolic pathways in an input graph was provided by Yang and Sze (2007).
Yang et al. [ 10] perform path matching and graph matching to query certain metabolic pathways or subgraphs in a predefined graph, but also use a similarity measure based on EC numbers only.
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Model transformation is one of the key tasks in model-driven engineering and relies on the efficient matching and modification of graph-based data structures; its sibling graph rewriting has been used to successfully model problems in a variety of domains.
The learning objective of the proposed method consists of a combination of three parts: the kernel matching criterion, prediction losses, and graph Laplacian regularization.
In the next section, we present the graph-theoretic framework of model reduction by graph matching, and its formal relationship to delete and merge operations on reaction graphs.
The core module provides data representations, graph analysis (i.e. graph matching and motif searching) and two different types of attribute comparator that differentiate in exact and approximate comparison.
Several methods, including random sample consensus (RANSAC) [44], Hough transform [28, 45], and graph transform matching (GTM) [30] refine initial correspondences based on SIFT.
Two well-known global stereo matching approaches, belief propagation [18] and graph cut [19], can produce high-quality disparity maps but require very high computational complexity.
Finally, the extracted structures are compared using the graph matching, and the most similar one is retrieved.
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