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The MSAGL software supplies four programming libraries: Microsoft.MSAGL.dll, a device-independent graph layout engine; Microsoft.MSAGL.Drawing.dll, a device-independent implementation of graphs as graphical user interface objects, with all kinds of graphical attributes, and support for interface events such as mouse actions;
GrGen.NET, the graph rewrite generator, a graph transformation tool emitting C#-code or .NET-assemblies. GROOVE, a Java-based tool set for editing graphs and graph transformation rules, exploring the state spaces of graph grammars, and model checking those state spaces; can also be used as a graph transformation engine.
The GraphLab framework is a parallel programming abstraction targeted for sparse iterative graph algorithms. GraphLab provides a programming interface, allowing deployment of distributed machine learning algorithms. [3] The main design considerations behind the design of GraphLab are: Sparse data with local dependencies; Iterative algorithms
A common example of a graph-based pathfinding algorithm is Dijkstra's algorithm. [3] This algorithm begins with a start node and an "open set" of candidate nodes. At each step, the node in the open set with the lowest distance from the start is examined.
3D version of Prim's algorithm. Vertical layers are labeled 1 through 4 from bottom to top. Stairs up are indicated with "/"; stairs down with "\", and stairs up-and-down with "x". Source code is included with the image description. Other algorithms exist that require only enough memory to store one line of a 2D maze or one plane of a 3D maze.
The traditional flood-fill algorithm takes three parameters: a start node, a target color, and a replacement color. The algorithm looks for all nodes in the array that are connected to the start node by a path of the target color and changes them to the replacement color.
The Library of Efficient Data types and Algorithms (LEDA) is a proprietarily-licensed software library providing C++ implementations of a broad variety of algorithms for graph theory and computational geometry. [1] It was originally developed by the Max Planck Institute for Informatics Saarbrücken. [2]
The algorithm continues until a removed node (thus the node with the lowest f value out of all fringe nodes) is a goal node. [b] The f value of that goal is then also the cost of the shortest path, since h at the goal is zero in an admissible heuristic. The algorithm described so far only gives the length of the shortest path.