Why should you use graphs?

In modern, data-driven solutions and enterprises, graph data structures are becoming more and more common. This is because, in our modern, data-driven world, relationships between things are becoming as, if not more important, than the things themselves. In modern industries and enterprises, graphs are starting to become more common and powerful in understanding the relationships between entities. I would say that these relationships and how they are connected have become more important than the entities themselves. We will demonstrate examples of real-life graphs in our use cases in the following chapters with detailed instructions on how to build these networks and the core considerations you need to make for the graph design.

Graphs are fundamental to many systems we use every day. Each time you are online and receive a product recommendation, it is likely that a graph solution is powering this recommendation. This is why learning how to work with graph data and leveraging these types of networks is a fast-growing and key skill in data science.

Composite components of a graph

Networks are a tool to represent complex systems and the complex nature of the connections arising in today’s data. We have already referenced how graphs are powering some of the big powerhouse recommendation systems in action today.

Graph methods tend to fall into four different areas:

• Movement: Movement is concerned with how things travel (move) through a network. These types of graphs are the drivers behind routing and GPS solutions and are utilized by the biggest players in finding the optimal path across a road network.
• Influence: On social media, this area specifies who the known influencers are and how they propagate this influence across a network.
• Groups and interactions: This area involves identifying groups and how actors in the network interact with each other. We will look at an example of how to apply community detection methods to find these communities through the node (the actor involved) and its connections (the edges). Don’t worry if you don’t know what these terms are for now; we will focus on these in the Fundamentals of nodes, edges, and the properties of a graph section.
• Pattern detection: Pattern detection involves using a graph to find similarities in the network that can be explored. We must look at this from the actor’s (node’s) point of view and find similarities between that actor and other actors in the network. Here, actor is taken to mean person, author profile, and so on.

In this section, we have explained the core components of a graph by providing simple working definitions. In the following section, we will delve deeper into these fundamental elements, which make up every graph you will come across in the industry. We will look at nodes, edges, and the various properties of a graph.