Description
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. Purposes of Graphs to resolve actual life issues.
3. Terminologies concerned in Graph Theory
4. Forms of Graph Knowledge Construction – Weighted, Unweighted, Directed, Undirected, Cyclic, Acyclic, Directed Acyclic Graphs.
This course additionally provides 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 based mostly on DFS – Topological Kind, 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.
Be aware: Data in Fundamental Knowledge Constructions and Python is most popular.
A graph knowledge construction consists of a finite (and presumably mutable) set of vertices (additionally referred to 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 often called edges (additionally referred to as hyperlinks or traces), and for a directed graph are also called edges but in addition generally 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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