Graph analytics is an analytical process that focuses on understanding relationships. It’s rooted in the mathematical concept of graph theory. There are two main components of a graph:
Graphs show you how multiple entities are related in detail. One natural output of graphs is pathways, which can identify possible routes between nodes. This has a number of applications. One familiar application is mapping tools that calculate the fastest way to your destination. Another application is social networks that show how someone you don’t know personally may be in your second or third degree network. In a business context, these relationships can show how different companies and organizations are connected to each other in a market space.
It might be easy to understand a handful of entities and relationships without graph analytics. But when there are thousands (or millions!) of entities and relationships, algorithms can crunch through this volume of data much more effectively. Algorithmic techniques in data science help identify the most important nodes within a network in a number of ways. This can include understanding many other nodes are connected to them or how many pathways have to pass through specific nodes. This can show critical entities and relationships that need to be understood.
Graph analytics allow us to map relationships in a way that is visually easy to understand. Once graphs are generated, we can use them to more quickly identify insights instead of sifting through lists or tables. Without the visual component, these important relationships could be missed in a sea of data.
Graph analytics and graph theory have applications in many fields, including linguistics, biology, and the social sciences (wiki). Check out some of the references for more detailed information.