Data Structure | 4. Model and Work with Graph Data Structures by Pavan | Learn Smarter
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4. Model and Work with Graph Data Structures

Graphs are fundamental data structures that model relationships in various applications like social networks and routing systems. They can be represented through adjacency lists or matrices, each with its advantages and disadvantages. Key traversal algorithms such as Depth-First Search and Breadth-First Search facilitate the exploration of graph structures, while advanced algorithms provide solutions for shortest paths and tree constructions.

Sections

  • 4

    Model And Work With Graph Data Structures

    This section introduces graph data structures, their types, representations, and traversal algorithms.

    Learning Practice

  • 4.1

    Introduction To Graphs

    Graphs are non-linear data structures that model networks through vertices and edges.

    Learning Practice

  • 4.2

    Types Of Graphs

    This section categorizes different types of graphs based on their properties and characteristics.

    Learning Practice

  • 4.3

    Graph Representations

    Graph representations are methods to model graphs using data structures like adjacency matrices and lists, each with its own advantages and disadvantages.

    Learning Practice

  • 4.4

    Graph Traversal Algorithms

    This section covers two primary graph traversal algorithms: Depth-First Search (DFS) and Breadth-First Search (BFS), including their implementations and applications.

    Learning Practice

  • 4.5

    Advanced Graph Algorithms (Conceptual Overview)

    This section introduces advanced algorithms for graph processing, focusing on shortest paths, minimum spanning trees, and topological sorting.

    Learning Practice

  • 4.6

    Graph Implementation Techniques

    This section explores various techniques for implementing graphs, including programming languages and libraries suitable for graph construction.

    Learning Practice

  • 4.7

    Applications Of Graphs

    Graphs are utilized across various domains to model relationships and solve complex problems.

    Learning Practice

  • 4.8

    Time And Space Complexities

    This section covers the time and space complexities of graph representations, specifically comparing adjacency lists and adjacency matrices.

    Learning Practice

  • 4.9

    Summary

    Graphs are crucial for modeling relationships and solving network-based challenges, with various representations and algorithms supporting their functionality.

    Learning Practice

References

ee-ds-4.pdf

Class Notes

Memorization

What we have learnt

  • Graphs consist of vertices ...
  • There are various types of ...
  • Key graph algorithms includ...

Final Test

Revision Tests