**Developing with Graph Algorithms**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 55m | 2.53 GB

Graphs are powerful data structures that we can use to model real-world relationships of all kinds. Through the paradigm of vertices (or nodes) that represent data, and edges (the connections between vertices), graphs can represent highly complex interconnections in nearly any environment, and you can see them in practical use in everything from social media apps (e.g., Facebook and LinkedIn) to the GPS apps in your phone and car. For each specific use, we can use algorithms that determine and direct how we use a graph, including, for example, algorithms that help networking systems determine the shortest path by which to send packet data to a destination, or those that make suggestions for new friends in your favorite social media app.

In this video course, designed for beginner- to intermediate-level developers and data scientists, host Mark Needham introduces graph algorithms and demonstrates how you can incorporate them into your software development and data science workflow. Your exploration begins by learning about three different categories of algorithms, including within them the world-famous PageRank algorithm, and going through some use cases that are particularly well suited for graph algorithms. You’ll see how to install Neo4j and the graph algorithms library as well as how you can use graph algorithms with Python in a Jupyter notebook. Later, Mark takes you through worked examples using each of the algorithms on real-world datasets. You’ll even get to apply your knowledge of graph algorithms by working through an end-to-end example on a Game of Thrones dataset, also involving graph visualization.

What you’ll learn—and how you can apply it

- The fundamentals of graphs and basic terminology
- Understand what graph algorithms are and learn about the kinds of problems you can solve by using them
- Three widely used categories of algorithms and many specific algorithms within them: pathfinding and graph search algorithms; centrality algorithms; and community detection algorithms
- How to execute graph algorithms against a sample dataset using Neo4j, NetworkX, and igraph
- How graph algorithms can be used with Python in a Jupyter notebook

This video course is for you because…

- You’re a software developer or data scientist who needs to make sense of connected data
- You’re tasked with developing an application that coordinates and controls many disparate interconnected data components
- You want to learn how you can integrate graph algorithms into a Python development environment

Prerequisites:

- You should have a beginner- to intermediate-level knowledge of software development practices
- You should have a familiarity with Python
- You should be comfortable using version control/Git

**Table of Contents**

01 Introduction

02 Module 1 Overview

03 What Are Graphs

04 What Are Graph Algorithms

05 Graph Algorithms Use Cases

06 Types of Graph Algorithms

07 Environment Setup

08 Module 1 Summary

09 Module 2 Overview

10 Introduction

11 Example Data

12 Breadth First Search

13 Depth First Search

14 Shortest Path

15 Shortest Path Variation – A

16 Yen’s k-Shortest Path

17 All Pairs Shortest Path

18 Single Source Shortest Path

19 Minimum Spanning Tree

20 Random Walk

21 Module 2 Summary

22 Module 3 Overview

23 Introduction

24 Example Data

25 Degree Centrality

26 Closeness Centrality

27 Betweenness Centrality

28 PageRank

29 Module 3 Summary

30 Checkpoint

31 Module 4 Overview

32 Introduction

33 Example Data

34 Triangle Count and Clustering Coefficient

35 Strongly Connected Components

36 Connected Components

37 Label Propagation

38 Louvain Modularity

39 Module 4 Summary

40 Module 5 Overview

41 Introduction to Yelp Dataset

42 Launching the Yelp Sandbox

43 Graph Model

44 Exploratory Analysis

45 Restaurants App

46 Sicilian Butcher Cross-Promotion

47 Finding Similar Categories

48 Module 5 Summary

49 Module 6 Overview

50 Introduction to Airlines Dataset

51 Importing Data

52 Exploratory Analysis

53 Popular Airports

54 Delays from ORD

55 Bad Day at SFO

56 Interconnected Airports by Airline

57 Module 6 Summary

58 Conclusion

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