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Graph Theory and it's Algorithms


I welcome you all to my course on ‘Graph Theory and it is Algorithms – Superior DSA’

This course offers with the ideas of Graph Theory reminiscent of

1. What’s Graph Knowledge Construction?

2. Functions of Graphs to unravel actual life issues.

3. Terminologies concerned in Graph Theory

4. Kinds of Graph Knowledge Construction – Weighted, Unweighted, Directed, Undirected, Cyclic, Acyclic, Directed Acyclic Graphs.

This course additionally offers the reason of the next algorithms and additionally present their implementation in Python.

1. Illustration of Graphs – Adjacency Listing, Adjacency Matrix.

2. Implementation of Adjacency Listing, Adjacency Matrix utilizing OOPS in Python.

3. Depth First Search (DFS) Algorithm in Python

4. Breadth First Search (BFS)

5. Issues primarily based on DFS – Topological Type, Sum, Max, Min.

Single Supply Shortest Path Issues.

1. Djikstra’s Algorithm – Algorithm and Code in Python.

2. Bellman Ford – Algorithm and Code in Python.

Minimal Spanning Tree Issues

1. Clarification of Spanning Timber, Discovering out Minimal Spanning Tree.

2. Prim’s and Kruskal’s Algorithm.

Notice: Information in Primary Knowledge Constructions and Python is most well-liked.

A graph information construction consists of a finite (and probably mutable) set of vertices (additionally known as nodes or factors), along with a set of unordered pairs of those vertices for an undirected graph or a set of ordered pairs for a directed graph. These pairs are referred to as edges (additionally known as hyperlinks or traces), and for a directed graph are also referred to as edges but additionally typically arrows or arcs. The vertices could also be a part of the graph construction, or could also be exterior entities represented by integer indices or references.



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