The need for representation learning

Here, we’ll introduce a new concept called representation learning for graphs. Let’s use a small analogy to understand what this means. A typical corporate organization has several entities: employees, IT equipment, offices, and so on. All these entities maintain different types of relationships with each other: employees can be related to each other based on organizational hierarchy; one employee may use several pieces of IT equipment; several pieces of equipment, such as servers, can be networked with each other; employees and equipment can report physically or be located in a particular office, respectively; and so on.

A graph, quite rightly, seems like a natural way to represent this information, like this:

Figure 1.8 – A graph showing the different entities in an organization interacting with each other

Graphs are very visually intuitive. However, performing algorithmic calculations on graphs...