This chapter covers:
- Introducing networks and graphs
- Introducing node degree characterization of a network
- Spotting graph-shaped tasks
- Introducing machine learning on graphs
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, ranging 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.
Also very 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.
