2 Representing network structure - design 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

The following visualization 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.
CH02 F01 path

The illustrated path in Figure 2.1 takes a bottom-up approach, where you 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

2.1 Graph terminology

2.2 Network representations

2.2.1 Labeled-property graph model

2.3 Designing your first labeled-property graph model

2.3.1 Follower network

2.3.2 User - Tweet network

2.3.3 Retweet network

2.3.4 Representing graph schema

2.4 Extracting knowledge from text

2.4.2 Hashtags

2.4.3 Mentions

2.4.4 Final Twitter social network schema

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