How do graph databases structure data?

Graph databases structure data in nodes, edges and properties, forming a graph structure that represents semantic relationships between data.

In more detail, a graph database is a type of NoSQL database that uses graph theory to store, map and query relationships. It is essentially a collection of nodes and edges. Each node represents an entity (such as a person or business) and each edge represents a connection or relationship between two nodes. Every node and edge can have an associated set of key/value pair properties, which can be used to add more information to the elements of the graph.

Nodes are the primary data elements in graph databases, and they are analogous to rows or records in a relational database. Nodes can have labels that indicate their types. For example, in a social network graph, the labels might be 'Person', 'Company', 'School', etc.

Edges, also known as relationships or links, are used to connect nodes and represent the relationships between them. They are directional and always have a start and end node. For example, in a social network graph, an edge might represent a 'FRIEND_OF' relationship between two 'Person' nodes.

Properties are additional pieces of information that can be attached to nodes and edges. They are represented as key-value pairs. For example, a 'Person' node might have properties such as 'name' and 'age', and a 'FRIEND_OF' edge might have a property such as 'since' to indicate when two people became friends.

The graph structure of graph databases allows for high performance when dealing with complex, interrelated data. Unlike relational databases, which require costly join operations to associate data, graph databases can easily navigate relationships between data points. This makes them particularly useful for use cases such as social networking, recommendation engines, and any other scenario where understanding the connections between data points is crucial.