Graph visualization is the visual representation of data - nodes and edges - stored as a graph. To better understand graph visualization, let’s start with the basics.
The first step to understanding graph visualization is understanding what a graph is. Also called network, a graph is a collection of nodes (or vertices) and edges - also called links or relationships. Each node represents a single data point such as a person, a phone number, or a transaction. Each edge represents how two nodes are connected: a person possesses a phone number for example.
Graph data is stored in a graph database such as Neo4j and Azure Cosmos DB.
Graph analytics provides algorithms that help data scientists and data-driven analysts answer questions or make predictions. This way of representing data is well suited for scenarios involving connections and networks of entities, like social networks, telecommunication networks, protein interactions, and much more.
Graph visualization is when the nodes and edges of a graph are displayed in a visual way. Dedicated algorithms, called layouts, calculate the node positions and display the data on two (sometimes three) dimensional spaces. Some examples of layouts are force-directed where larger or more important elements are closer to the center, or radial layout, where nodes are arranged in concentric circles, showing dependencies.