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Viewing a function as a functional graph provides a convenient language for describing properties that are not as easily described from the function-theoretic point of view; this technique is especially applicable to problems involving iterated functions, which correspond to paths in functional graphs.
A functional graph database is any evaluation of these variables.
Thus, a functional graph database schema is a set of variables of types from T restricted to attribute types.
For convenience, we denote the variables from the functional graph database schema by names remaining entity and relationship types in the associated traditional GDB schema or at least their abbreviations.
In the current era of GDBMSs, we can mention the Gremlin3 a functional graph query language developed by Apache TinkerPop which allows to express complex graph traversals and mutation operations over property graphs.
Aware of the fact that graph operations is a core of chemoinformatics, FragmenT represents chemical structures by means of Functional Graph Library – a powerful tool for graph operations [2].
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For this reason, maximal directed pseudoforests are sometimes called functional graphs.
Another early application of functional graphs is in the trains used to study Steiner triple systems.
In these applications, ƒ is expected to behave randomly; Flajolet and Odlyzko study the graph-theoretic properties of the functional graphs arising from randomly chosen mappings.
These functional graphs, which they call state transition diagrams, have one vertex for each possible configuration that the ensemble of cells of the automaton can be in, and an edge connecting each configuration to the configuration that follows it according to the automaton's rule.
Thus, a graph database schema can reflect a reality only partially, similarly to non-functional graph data modelling.
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