Graph Data Science with Python and Neo4j: Hands-on Projects on Python and Neo4j Integration for Data Visualization and Analysis Using Graph Data Science for Building Enterprise Strategies (English Edition)

CHAPTER 1

Introduction to Graph Data Science

Introduction

In this chapter, we will provide an introduction and overview of graph data science as a method to explore contextual relationships in data. We will explore the significance and versatility of graphs in various domains. Our daily lives are full of graphs, from social media to the maps we use to drive to work, to the recommendations provided to us on our favorite TV streaming network.

We will analyze Python and Neo4j as the tools to learn and explore graphs. These tools offer extensive libraries as well as robust community support, which makes them a great choice for the journey of graph data science.

Structure

In this chapter, the following topics will be covered:

Understanding Graphs, Graph Networks, and their Relevance

Introduction to Neo4j Graph Database

Overview of the Importance of Graph Visualizations

Data Science and Machine Learning

Introduction to Graph Data Science

Introduction to the Python Programming Language

Data Science and Machine Learning

Before we jump into the fascinating world of graph data science, it’s important to clarify two fundamental terms: data science and machine learning. While we assume a certain level of familiarity with these concepts, we will guide you along the way.

Data Science is a multidisciplinary field that involves extracting knowledge and insights from data through various techniques such as data mining, data visualization, and statistical analysis. Data science involves the end-to-end process of acquiring, cleaning, transforming, and analyzing data to uncover patterns, make predictions, and drive better decision-making:

Figure 1.1: Visual of end-to-end data analysis (Source: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRXeY_2rpyHpnH9QJYk61usUIQ1NTXrWrQefA&usqp=CAU)

Machine Learning, on the other hand, is a subset of data science that focuses on developing algorithms and models that enable computers to learn from data and make predictions or take actions without being explicitly programmed. Machine learning algorithms learn from historical data to identify patterns, make predictions, and automate decision-making processes on new, never-before-seen data.

Figure 1.2: Visual of a computer processing a large amount of historical data and then exporting predictions (Source: https://www.dataversity.net/future-analytics-hype-real/)

In summary, while the two are often used as synonyms, data science provides the foundation and tools to explore, interpret, and gain insights from data, while machine learning leverages the data to build predictive models and make accurate predictions and/or automated decisions. Together, the two form a powerful combination that drives innovation and enables data-driven solutions.

Defining Graph

While you might first think of a graph as a pie chart or an x and y axis, we refer to a graph in this book as something else entirely. In discrete mathematics and graph theory, a graph is a structure that consists of objects or nodes (illustrated as dots in Figure 1.3) where pairs of objects or nodes are connected or related in some way. These objects can be referred to as vertices, nodes, or points. In this book, we will refer to these objects as