Book Image

Practical Discrete Mathematics

By : Ryan T. White, Archana Tikayat Ray
Book Image

Practical Discrete Mathematics

By: Ryan T. White, Archana Tikayat Ray

Overview of this book

Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks. Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level. As you learn the language of discrete mathematics, you’ll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you’ll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science. By the end of this book, you’ll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning.
Table of Contents (17 chapters)
1
Part I – Basic Concepts of Discrete Math
7
Part II – Implementing Discrete Mathematics in Data and Computer Science
12
Part III – Real-World Applications of Discrete Mathematics

Chapter 8: Storage and Feature Extraction of Graphs, Trees, and Networks

The structures we will learn about in this chapter all stem from the idea of a graph, which is a pair of sets of nodes (called vertices) and connections (called edges) linking nodes together. As we will see in this chapter and the following chapters graphs, and their variations are useful for modeling many real situations and solving practical problems in computer and data sciences.

The following topics will be covered in this chapter:

  • Understanding the terminology and notation of graphs, trees, and networks
  • An overview of some ways graph and network models are used in real problems
  • Efficient storage of graphs of networks in Python
  • Using Python to extract features of graphs or networks

By the end of the chapter, you should be able to differentiate between graphs, trees, networks, and directed versions of them, be familiar with common applications of these structures as models for...