Graphs - Abstract Data Type

This library contains a collection of Graph abstract data types, each with a set of algorithms.

Graphs

A graph is a structure amounting to a set of objects in which some pairs of the objects are in some sense "related" (Wikipedia), 2019).

$3

A directed or undirected graph data structure.

#### Options
- directed - _default: false_

``js
const g = new Graph({
directed: true
})
`

#### Methods
- g.addNode(key: string)
- Creates and adds a new node into the graph
- g.getNode(key: string)
- Returns the requested node
- g.addEdge(key1: string, key2: string, weight: number)
- Creates a new edge between key1 and key2 with the desired weighting. In a directed graph the edge will be directed from key1 to key2
- g.getEdge(key1: string, key2: string)
- Returns the weighting of the requested edge. If the graph is directed it will return the weighting from key1 to key2. If the graph is undirected the ordering of the node keys doesn't matter.
- g.getPath(key1: string, key2: string)
- Returns an array of nodes ordered by the shortest path based on the weight of the edges
- g.dijkstra(key: string)
- Returns an object keyed by all nodes possible to connect to the node represented by key. Each node key maps to data representing the distance from key and the last traveled
- g.bfs(key: string, fn: Function)
- Breadth first traversal where key is the starting node and fn is the callback function to run on each visited node
- g.dfs(key: string, fn: Function)
- Depth first traversal where key is the starting node and fn is the callback function to run on each visited node

$3

- Graph
- A*
- Tree
- Binary
- Red-black

Implementing a Graph

Install the package

`
$ npm install -S graphs-adt
`

Create a graph and add some nodes and edges. In this example we will add locations in England and the distance between them to represent the weight.

`js
import { Graph } from 'graphs-adt';

// create a new instance of an undirected graph
const g = new Graph();

// adding nodes (vertices)
g.addNode('London');
g.addNode('Bristol');
g.addNode('Bath');
g.addNode('Bournemouth');

// adding edges (links)
g.addEdge('London', 'Bath', 114);
g.addEdge('Bath', 'Bristol', 23);
g.addEdge('Bristol', 'Bournemouth', 73);
g.addEdge('Bournemouth', 'London', 101);
g.addEdge('Bournemouth', 'Bath', 62);
`

Now we have a simple graph that represents the distance between each City. A visualisation of this graph may look like the following.

_Note the length of the edges do NOT represent the weighting, but the numbers do. Further, there are no arrow heads on the diagram as the graph is undirected so the edges are bidirectional._

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If we now want to shortest distance from London to Bristol we can use the getPath method. Currently, by default this uses Dijkstra's algorithm.

`js
g.getPath('London', 'Bristol');

// [ 'London', 'Bath', 'Bristol' ]
`

It is also possible just to run the Dijkstra algorithm on a node and get the distances to each node it can reach.

`js
g.dijkstra('London')

// { London: { distance: 0, previous: null },
// Bristol: { distance: 137, previous: 'Bath' },
// Bath: { distance: 114, previous: 'London' },
// Bournemouth: { distance: 101, previous: 'London' } }
`

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All graphs support breadth first and depth first searching methods. Just call the method with a key of the node you wish to start at and a callback function.

The following will log out each node to the console starting from the London Node.

`js
// depth first search
g.dfs('London', node => {
console.log('Node:', node)
});

// breadth first search
g.bfs('London', node => {
console.log('Node:', node)
});
``