Book Image

Learn Data Structures and Algorithms with Golang

By : Bhagvan Kommadi
Book Image

Learn Data Structures and Algorithms with Golang

By: Bhagvan Kommadi

Overview of this book

Golang is one of the fastest growing programming languages in the software industry. Its speed, simplicity, and reliability make it the perfect choice for building robust applications. This brings the need to have a solid foundation in data structures and algorithms with Go so as to build scalable applications. Complete with hands-on tutorials, this book will guide you in using the best data structures and algorithms for problem solving. The book begins with an introduction to Go data structures and algorithms. You'll learn how to store data using linked lists, arrays, stacks, and queues. Moving ahead, you'll discover how to implement sorting and searching algorithms, followed by binary search trees. This book will also help you improve the performance of your applications by stringing data types and implementing hash structures in algorithm design. Finally, you'll be able to apply traditional data structures to solve real-world problems. By the end of the book, you'll have become adept at implementing classic data structures and algorithms in Go, propelling you to become a confident Go programmer.
Table of Contents (16 chapters)
Free Chapter
Section 1: Introduction to Data Structures and Algorithms and the Go Language
Section 2: Basic Data Structures and Algorithms using Go
Section 3: Advanced Data Structures and Algorithms using Go

Network and Sparse Matrix Representation

A sparse matrix is a matrix in which most of the values are zero. The ratio of zero values to non-zero values is known as the sparsity. An estimation of a matrix's sparsity can be helpful when creating hypotheses about the availability of networks. Extensive big sparse matrices are commonly used in machine learning and natural language parsing. It is computationally costly to work with them. Recommendation engines use them for representing products inside a catalog. Computer vision uses sparse matrices and network data structures when working with pictures that contain sections with dark pixels. Network and sparse matrix data structures are also used in social graphs and map layouts. In this chapter, we will cover the following topics:

  • Network representations using graphs:
    • Social network representation
    • Map layouts
    • Knowledge graphs...