3 Graphs in machine learning applications

 

This chapter covers

  • The role of graphs in the machine learning workflow
  • How to store the training data and the resulting model properly
  • Graph-based algorithms for machine learning
  • Data analysis with graph visualization

In this chapter, we’ll explore in more detail how graphs and machine learning can fit together, helping to deliver better services to end users, data analysts, and businesspeople. Chapters 1 and 2 introduced general concepts in machine learning, such as

  • The different phases that compose a generic machine learning project (specifically, the six phases of the CRISP-DM model: business understanding, data understanding, data preparation, modeling, evaluation, and deployment)
  • The importance of data quality and quantity to create a valuable and meaningful model that can provide accurate predictions
  • How to handle a large amount of data (big data) by using a graph data model

Here, we will see how to harness the power of the graph model as a way of representing data that makes it easy to access and analyze, as well as how to use the “intelligence” of the machine learning algorithms based on graph theory. I would like to start this chapter with an image (figure 3.1) that represents the path of converting raw data, available from multiple sources, to something that is more than simple knowledge or insight: wisdom.

Figure 3.1 Illustration by David Somerville, based on the original by Hugh McLeod1
CH03_F01_Negro

1.www.smrvl.com.

3.1 Graphs in the machine learning workflow

3.2 Managing data sources

3.2.1 Monitor a subject

3.2.2 Detect a fraud

3.2.3 Identify risks in a supply chain

3.2.4 Recommend items

3.3 Algorithms

3.3.1 Identify risks in a supply chain

3.3.2 Find keywords in a document

3.3.3 Monitor a subject

3.4 Storing and accessing machine learning models

3.4.1 Recommend items

3.4.2 Monitoring a subject

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