Graph modeling guidelines
Introduction
If you have ever worked with an object model or an entityrelationship diagram, the labeled property graph model will seem familiar.
Graph data modeling is the process in which a user describes an arbitrary domain as a connected graph of nodes and relationships with properties and labels. A Neo4j graph data model is designed to answer questions in the form of Cypher queries and solve business and technical problems by organizing a data structure for the graph database.
Graph data model = whiteboardfriendly
The graph data model is often referred to as being "whiteboardfriendly". Typically, when designing a data model, people draw example data on the whiteboard and connect it to other data drawn to show how different items connect. The whiteboard model is then reformatted and structured to fit normalized tables for a relational model.
A similar process exists in graph data modeling, as well. However, instead of modifying the data model to fit a normalized table structure, the graph data model stays exactly as it was drawn on the whiteboard. This is where the graph data model gets its name for being "whiteboardfriendly".
Let us look at an example to demonstrate this. In the whiteboard drawing below, we have a data set about the movie "The Matrix".
Next, we formalize our entities a bit and match expected syntax for relationship types to create the node/relationship view for the property graph model.
For our next step, we add labels and determine properties of our nodes and relationships for the property graph model.
Finally, we can view this data model in Neo4j and ensure it matches what we drew on the whiteboard. Also, notice how it is nearly identical to the whiteboard model we initially designed.
The ability to easily whiteboard your data model makes the graph data model incredibly simple and visual. There is no need to draw up business model versions or explain ERD terms to business users. Instead, the graph data model is easily understood by anyone.
Describing a domain
To better understand the process of designing a graph data model, let us take an example domain for a small set of data and walk through each step of how to create a graph data model from it. Consider the following scenario describing our example data entities and connections.
Two people, Sally and John, are friends. Both John and Sally have read the book, Graph Databases.
We can use the information in this statement to build our model by identifying the components as labels, nodes, and relationships. Let us take the scenario into pieces and define them as parts of our property graph model.
Nodes
The first entities that we identify in our domain are the nodes. Nodes are one of two fundamental units that form a graph (the other fundamental unit is relationships).
Nodes are often used to represent entities, but can also represent other domain components, depending on the use case. Nodes can contain properties that hold namevalue pairs of data. Nodes can be assigned roles or types using one or more labels.
We can identify nodes as entities with a unique conceptual identity. In our scenario we began for Sally and John, these entities are outlined below in bold.
Two people, John and Sally, are friends. Both John and Sally have read the book, Graph Databases.
Extracting the nodes:
* John
* Sally
* Graph Databases
Labels
Now when we have an idea of what our nodes will be, we can decide what labels (if any) to assign our nodes to group or categorize them.
Let us remind the definition of what labels do and how they are used in the graph data model.
A label is a named graph construct that is used to group nodes into sets.
All nodes labeled with the same label belongs to the same set.
Many database queries can work with these sets instead of the whole graph, making queries easier to write and more efficient. A node may be labeled with any number of labels, including none, making labels an optional addition to the graph.
To find out if we can group objects in our Sally and John scenario, we start by identifying the roles of our nodes (John, Sally, Graph Databases) mentioned in the statement. We can find two different types of objects in the statement, which are emphasized below.
Two people, John and Sally, are friends. Both John and Sally have read the book, Graph Databases.
Extracting the labels:
* Person
* Book
Now that we have identified both our nodes and labels, we can update our graph data model to assign the labels to the nodes they describe. For John and Sally, we apply the label Person. For Graph Databases, we apply the label Book.
Relationships
We now have our main entities and a way to group them, but we are still missing one vital piece of a graph database model  the relationships between the data!
A relationship connects two nodes and allows us to find related nodes of data. It has a source node and a target node that shows the direction of the arrow. Although you must store a relationship in a particular direction, Neo4j has equal traversal performance in either direction, so you can query the relationship without specifying direction.
The one core, consistent rule in a graph database is "No broken links", ensuring that an existing relationship will never point to a nonexisting endpoint. Since a relationship always has a start and end node, you cannot delete a node without also deleting its associated relationships.
Let us identify the interactions (which are underlined in our scenario below) between the John, Sally, and Graph Database nodes.
Two people, Sally and John, are friends. Both John and Sally have read the book, Graph Databases.
Relationships between nodes:
* John is friends with Sally
* Sally is friends with John
* John has read Graph Databases
* Sally has read Graph Databases
To sum up our findings, our John and Sally nodes (labeled Person) can be connected to each other by the is friends with relationship. John and Sally have both read the Graph Databases book, so we can connect each of their nodes (each labeled Person) to the Graph Databases node (labeled Book) with a has read relationship.
Properties
We have gone through the process of creating a basic graph data model for the interactions between people and books. We can take this data model further by defining attributes of these entities as keyvalue properties.
Properties are namevalue pairs of data that you can store on nodes or on relationships. Most standard data types are supported as properties, and you can find information on that in the section Graph database concepts^.
Properties allow you to store relevant data about the node or relationship with the entity it describes. They can often be found by knowing what kinds of questions your use case needs to ask of your data.
For our John and Sally scenario, we can list some questions that we might want to answer about the data.

When did John and Sally become friends? Or how long have they been friends?

What is the average rating of the Graph Databases book?

Who is the author of the Graph Databases book?

How old is Sally?

How old is John?

Who is older, Sally or John?

Who read the Graph Databases book first, Sally or John?
From this list of questions, you can identify the attributes that we need to store on the entities within our data model in order to answer these questions.
With the final model, we now can answer each of the questions we defined in our list. Of course, we can grow and change the model over time and add/remove relationships, nodes, properties, and labels. The flexibility and simplicity of the property graph data model allows users to easily review the data structure and update it according to the changing needs of the business.
Summary
That is the introduction to data modeling using a simple, straightforward scenario. There are plenty of opportunities throughout the upcoming sections to practice modeling domains and analyzing changes to the model that might need to be made.
Every data model is unique, depending on the use case and the types of questions that users need to answer with the data. Because of this, there is no "onesizefitsall" approach to data modeling. Using best practices and careful modeling will provide the most valuable result in producing an accurate data model that benefits your processes and use case. A walkthrough of designs for different use cases is in the following section.
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