1 Graphs and network science: An introduction

 

This chapter covers:

  • Introducing networks and graphs
  • Introducing network science
  • Introducing graph analytics
  • Network analysis workflow overview

Networks are everywhere, and they do matter. First of all, where are these networks? Communication networks are one example. For example, the internet consists of routers. Routers analyze the incoming data, determine the optimal path to the destination, and forward the data to the next device along the route. Another example are the social media platforms. You use those platforms to connect with other users. Most of your connections are local, spanning from your family and friends to coworkers. And then you have some connections from distant friends that can span oceans and continents. When you map all those connections, what you end up with is referred to as a social network.

Figure 1.1. World-wide social network
CH01 F01 socialnetwork

Maybe most interestingly, your biological existence depends on networks. Proteins are called the building blocks of the body. They form the machinery that helps sustain life. Proteins rarely act alone as their functions tend to be regulated. The identification of protein interactions can lead to a better understanding of diseases and the development of drugs and treatments. The process of mapping those interactions results in protein-protein interaction networks, also known as PPI.

Figure 1.2. Protein-protein interaction network
CH01 F02 ppinetwork

1.1 Introduction to graph theory

1.1.1 What is a graph?

1.1.2 Graph terminology

1.2 The beautiful world of network science

1.2.1 Brief history of network science

1.2.2 Real-world network science application

1.3 Graph analytics toolbox

1.3.1 Graph-based query patterns

1.3.2 Graph algorithms

1.4 Network analysis workflow

1.4.1 How to spot a graph-shaped problem

1.4.2 Graph model

1.4.3 Graph construction

1.4.4 Network analysis

1.4.5 Using network features in a downstream ML workflow

1.5 Summary

1.6 References

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