Property Graph

In a property graph, nodes (or vertices) represent discrete entities such as a Person, Product, or Event. Each node can have multiple properties, which are key-value pairs (e.g., name: "Alice", age: 34, department: "Engineering"). This allows for the storage of detailed, attribute-rich data directly on the entity without requiring a rigid, pre-defined schema. Nodes are typically labeled (e.g., :Person) to categorize them for efficient querying.

Relationships (or edges) are first-class citizens that explicitly connect nodes. They are always directed (having a start and end node) and typed (e.g., WORKS_FOR, PURCHASED, LOCATED_IN). This directionality and explicit semantics are crucial for accurately modeling real-world connections like workflows, dependencies, or social networks. Relationships are not mere associations; they define the precise nature of the connection between entities.

Unlike simpler graph models, relationships in a property graph can also have their own properties. This is a powerful feature for capturing metadata about the connection itself. For example, a PURCHASED relationship could have properties like date: "2024-01-15", amount: 299.99, and method: "credit_card". This allows the graph to store not just that a connection exists, but the contextual details of how and when it occurred.

Property graphs support a schema-optional or schema-late approach. You can insert data with any structure immediately, enabling rapid prototyping and handling of heterogeneous data sources. Schema constraints (like allowed node labels, relationship types, and property keys) can be enforced later for data quality and integrity as the application matures. This balances developer agility with production robustness.

A key performance characteristic of native graph databases is index-free adjacency. This means a node contains direct physical pointers to its connected relationships and neighboring nodes. Traversing from one node to another is a constant-time O(1) operation, as the database follows these stored pointers rather than performing expensive global index lookups. This makes complex, multi-hop graph traversals extremely fast, regardless of the total dataset size.

A structured semantic network that represents real-world entities as nodes and their interrelationships as edges. While a property graph is the underlying data model, a knowledge graph typically implies a rich, domain-specific ontology and is used for logical reasoning and contextual understanding. It transforms raw connected data into a machine-readable web of meaning.

• Core Purpose: Enable symbolic reasoning and answer complex, multi-hop queries.
• Example: A biomedical knowledge graph connecting drugs, proteins, diseases, and side effects to accelerate drug discovery.

A database designed specifically to store and query data in the form of subject-predicate-object triples, which is the foundational data model of the Semantic Web. Unlike the flexible, schema-optional property graph, RDF imposes a more formalized, standardized structure.

• Key Differentiator: Uses globally unique URIs for identifiers and is queried with SPARQL.
• Use Case: Integrating and linking heterogeneous data from multiple public sources on the web, where standardization is critical.

The computational process of visiting nodes in a graph data structure by following the edges that connect them. This is the primary query mechanism for property graphs, allowing systems to navigate relationships to find paths, neighborhoods, or connected components.

• Algorithms: Include breadth-first search (BFS), depth-first search (DFS), and more complex pathfinding algorithms.
• Performance: Efficient traversal is enabled by index-free adjacency, where each node contains direct pointers to its connected nodes.

A formal, explicit specification of a shared conceptualization within a domain. It defines the types of entities (classes), their properties, and the relationships between them. An ontology provides the schema or taxonomy for a knowledge graph, giving meaning to the nodes and edges in a property graph.

• Role: Serves as a contract for meaning, ensuring different systems and agents interpret graph data consistently.
• Standard: Often expressed in languages like OWL (Web Ontology Language).