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The basic idea is that if the state of a computation can be represented as a graph, further steps in that computation can then be represented as transformation rules on that graph. Such rules consist of an original graph, which is to be matched to a subgraph in the complete state, and a replacing graph, which will replace the matched subgraph.
Double pushout graph rewriting allows the specification of graph transformations by specifying a pattern of fixed size and composition to be found and replaced, where part of the pattern can be preserved. The application of a rule is potentially non-deterministic: several distinct matches can be possible.
GSS (Graph Style Sheets) in mathematics and computing, is an RDF (Resource Description Framework) vocabulary for representation of data in a model of labeled directed graph. [1] Using it will make a relatively complex data resource modeled in RDF , much easier to understand by declaring simple styling and visibility instructions to be applied ...
The transformation of graphs is often formalized and represented by graph rewrite systems. Complementary to graph transformation systems focusing on rule-based in-memory manipulation of graphs are graph databases geared towards transaction-safe, persistent storing and querying of graph-structured data.
The Q-treatments are applied in sequence, unless one of them produces the empty Q-graph, in which case the result is the last Q-graph obtained. The three parts of a rule can contain variables for labels, trees, and forests. All variables after "==" must appear in the <matched_path> part. Variables are local to rules.
ΔY- and YΔ-transformations are a tool both in pure graph theory as well as applications. Both operations preserve a number of natural topological properties of graphs. . For example, applying a YΔ-transformation to a 3-vertex of a planar graph, or a ΔY-transformation to a triangular face of a planar graph, results again in a planar graph.
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The reciprocal transformation, some power transformations such as the Yeo–Johnson transformation, and certain other transformations such as applying the inverse hyperbolic sine, can be meaningfully applied to data that include both positive and negative values [10] (the power transformation is invertible over all real numbers if λ is an odd ...