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Python Data Structures and Algorithms

You're reading from   Python Data Structures and Algorithms Improve application performance with graphs, stacks, and queues

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781786467355
Length 310 pages
Edition 1st Edition
Languages
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Author (1):
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Benjamin Baka Benjamin Baka
Author Profile Icon Benjamin Baka
Benjamin Baka
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Table of Contents (20) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Python Objects, Types, and Expressions FREE CHAPTER 2. Python Data Types and Structures 3. Principles of Algorithm Design 4. Lists and Pointer Structures 5. Stacks and Queues 6. Trees 7. Hashing and Symbol Tables 8. Graphs and Other Algorithms 9. Searching 10. Sorting 11. Selection Algorithms 12. Design Techniques and Strategies 13. Implementations, Applications, and Tools

Graphs


A graph is a set of vertices and edges that form connections between the vertices. In a more formal approach, a graph G is an ordered pair of a set V of vertices and a set E of edges given as G = (V, E) in formal mathematical notation.

An example of a graph is given here:

Let's now go through some definitions of a graph:

  • Node or vertex: A point, usually represented by a dot in a graph. The vertices or nodes are A, B, C, D, and E.
  • Edge: This is a connection between two vertices. The line connecting A and B is an example of an edge.
  • Loop: When an edge from a node is incident on itself, that edge forms a loop.
  • Degree of a vertex: This is the number of vertices that are incident on a given vertex. The degree of vertex B is 4.
  • Adjacency: This refers to the connection(s) between a node and its neighbor. The node C is adjacent to node A because there is an edge between them.
  • Path: A sequence of vertices where each adjacent pair is connected by an edge.
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