2 Representing network structure: Designing your first graph model

This chapter covers

Introducing the learning path to mastering graph algorithms and data science
Getting familiar with basic graph terminology
Labeled-property graph model schema design
Extracting information from tweets

Figure 2.1 illustrates how I envision the learning path to becoming a versatile and experienced graph data practitioner and scientist. This book will take you along this exciting path.

Figure 2.1 Learning path to mastering graph algorithms and data science

The path illustrated in figure 2.1 takes a bottom-up approach, where you will first learn to describe the data for your domain as a graph, encompassing modeling and constructing a graph. Next, you will learn to identify, retrieve, and aggregate various graph patterns. Once the basics are out of the way, you will dig into descriptive graph analytics, which can help you understand the current state of the graph. In the last couple of chapters, you will learn how to combine all the previous lessons and apply them to predict new patterns in the graph.

The learning path consists of four major milestones:

Graph modeling and construction
Graph query language
Graph algorithms and inferred networks
Graph machine learning

2 Representing network structure: Designing your first graph model

This chapter covers

Figure 2.1 Learning path to mastering graph algorithms and data science

2.1 Graph terminology

2.1.1 Directed vs. undirected graph

2.1.2 Weighted vs. unweighted graphs

2.1.3 Bipartite vs. monopartite graphs

2.1.4 Multigraph vs. simple graph

2.1.5 A complete graph

2.2 Network representations