Graph-based Data Structure

Context:
- It can support Graph Data Structure Operations, such as: add_node, add_edge, ...
- It can range from being a Directed Graph Data Structure (such as a Tree DS) to being a Undirected Graph Data Structure.
- It can be used to represent a Tree Data Structure, such as a Decision Tree Data Structure.
- It can range from being a Labeled Graph Structure to being an Unlabeled Graph Structure.
- It can be managed by a Graph Data Management System.
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Example(s):
- an Acyclic Graph Data Structure, such as a lattice data structure, and a text graph data structure.
- a Makefile.
- a Graphical Statistical Model Structure.
- a TensorFlow Graph.
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Counter-Example(s):
- a Timeseries Data Structure.
See: Graph Data Pattern, Graph-based Knowledge Representation System, Hypergraph.

References

(Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Graph_(abstract_data_type) Retrieved:2017-6-8.
- In computer science, a graph is an abstract data type that is meant to implement the undirected graph and directed graph concepts from mathematics, specifically the field of graph theory.
  A graph data structure consists of a finite (and possibly mutable) set of vertices or nodes or points, together with a set of unordered pairs of these vertices for an undirected graph or a set of ordered pairs for a directed graph. These pairs are known as edges, arcs, or lines for an undirected graph and as arrows, directed edges, directed arcs, or directed lines for a directed graph. The vertices may be part of the graph structure, or may be external entities represented by integer indices or references.
  A graph data structure may also associate to each edge some edge value, such as a symbolic label or a numeric attribute (cost, capacity, length, etc.).