9. Graphs | Learning JavaScript Data Structures and Algorithms

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Table Of Contents

Learning JavaScript Data Structures and Algorithms - Second Edition

By : Loiane Avancini

4 (10)

Learning JavaScript Data Structures and Algorithms

4 (10)

By: Loiane Avancini

Overview of this book

This book begins by covering basics of the JavaScript language and introducing ECMAScript 7, before gradually moving on to the current implementations of ECMAScript 6. You will gain an in-depth knowledge of how hash tables and set data structure functions, as well as how trees and hash maps can be used to search files in a HD or represent a database. This book is an accessible route deeper into JavaScript. Graphs being one of the most complex data structures you’ll encounter, we’ll also give you a better understanding of why and how graphs are largely used in GPS navigation systems in social networks. Toward the end of the book, you’ll discover how all the theories presented by this book can be applied in real-world solutions while working on your own computer networks and Facebook searches.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. JavaScript—A Quick Overview

1. JavaScript—A Quick Overview

JavaScript data structure and algorithms

Setting up the environment

JavaScript basics

Control structures

Functions

Object-oriented programming in Javascript

Debugging and tools

Introducing ECMAScript

ECMAScript 6 functionalities

ECMAScript 7 functionalities

Summary

2. Arrays

2. Arrays

Why should we use arrays?

Creating and initializing arrays

Adding elements

Removing elements

Adding and removing elements from a specific position

Two-dimensional and multidimensional arrays

References for JavaScript array methods

The TypedArray class

Summary

3. Stacks

3. Stacks

The stack data structure

EcmaScript 6 and the Stack class

Solving problems using stacks

Summary

4. Queues

4. Queues

The queue data structure

Creating a queue

The Queue class using ECMAScript 6 syntax

The priority queue

The circular queue - Hot Potato

JavaScript task queues

Summary

5. Linked Lists

5. Linked Lists

The linked list data structure

Creating a linked list

Doubly linked lists

Circular linked lists

Summary

6. Sets

6. Sets

Structuring a dataset

Creating a set

Set operations

ES6 – the Set class

Summary

7. Dictionaries and Hashes

7. Dictionaries and Hashes

Dictionaries

The hash table

The ES6 Map class

The ES6 WeakMap and WeakSet classes

Summary

8. Trees

8. Trees

The tree data structure

Tree terminology

The binary and binary search trees

Tree traversal

Searching for values in a tree

Self-balancing trees

Summary

9. Graphs

9. Graphs

Graph terminology

Representing a graph

Creating the Graph class

Graph traversals

Shortest path algorithms

Minimum spanning tree (MST)

Summary

10. Sorting and Searching Algorithms

10. Sorting and Searching Algorithms

The sorting algorithms

Searching algorithms

Summary

11. Patterns of Algorithm

11. Patterns of Algorithm

Recursion

Dynamic programming

Greedy algorithms

Introduction to functional programming

Summary

12. Algorithm Complexity

12. Algorithm Complexity

Big-O notation

Having fun with algorithms

Summary

Representing a graph

There are a few ways in which we can represent graphs when it comes to data structures. There is no correct way of representing a graph among the existing possibilities. It depends on the type of problem you need to resolve and the type of graph as well.

The adjacency matrix

The most common implementation is the adjacency matrix. Each node is associated with an integer, which is the array index. We will represent the connectivity between vertices using a two-dimensional array, as array[i][j] = = = 1 if there is an edge from the node with index i to the node with index j or as array[i][j] = = = 0 otherwise, as demonstrated by the following diagram:

Graphs that are not strongly connected (sparse graphs) will be represented by a matrix with many zero entries in the adjacency matrix. This means we would waste space in the computer memory to represent edges that do not exist; for example, if we need to find the adjacent vertices of a given vertex, we will have to iterate through...

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