What is a graph database?
A graph database is a type of database designed to store and query connected data. Instead of organising information into rows and tables, it models data as entities and the relationships between them.
In a graph database, people, accounts, devices, locations, organisations, and events can all be represented as connected parts of the same dataset. This makes it much easier to explore how things relate to one another, trace paths between entities, and uncover patterns that would be difficult to spot in disconnected systems.
Graph databases are particularly useful when the relationships between records are as important as the records themselves. That makes them a strong fit for fraud detection, criminal intelligence, cybersecurity, knowledge graphs, and other analytical environments where investigators need to understand networks, not just individual records.
How does a graph database work?
A graph database typically stores data using three core elements:
Nodes
Nodes represent entities such as a person, bank account, phone number, company, device, or location.
Relationships
Relationships describe how those entities are connected. For example, a person may own a company, share an address with another person, call a phone number, or transfer money to a bank account.
Properties
Properties store details about nodes and relationships. A person node might include a name, date of birth, or identifier. A transaction relationship might include a timestamp, amount, or payment reference.
This structure makes graph databases well suited to modelling real-world scenarios. Instead of reconstructing relationships through multiple joins and lookups, analysts can follow connections directly through the graph.
For example, in an investigation, a graph database might show that a suspect is linked to a vehicle, that vehicle is linked to an address, and that address is linked to another person already known to investigators. Rather than searching each dataset separately, the graph makes those connections visible as part of a connected whole.
Graph database vs relational database
Both graph databases and relational databases store and manage data, but they are designed for different kinds of problems.
A relational database stores data in tables. Relationships between records are created using keys and joins. This works well for structured, repeatable business data such as transactions, inventory, payroll, or customer records.
A graph database stores data as nodes and relationships. This makes it better suited to questions that depend on how entities are connected, especially when those connections span multiple systems or many degrees of separation.
For example, a relational database can tell you which account belongs to which customer. A graph database can help you explore how that customer is connected to other accounts, devices, addresses, companies, transactions, and events across a wider network.
In practice, the difference is not that one replaces the other. Relational databases are often the system of record for operational data. Graph databases are useful when you need to integrate connected information from multiple sources and analyse the relationships between them.
When a graph database is especially useful
A graph database is often a good fit when you need to:
- Understand how people, organisations, devices, locations, and events are connected
- Investigate multi-step relationships across several datasets
- Identify suspicious patterns, clusters, or hidden intermediaries
- Analyse networks such as co-offending groups, ownership structures, or payment flows
- Ask questions like “how is this entity connected to that one?” or “what links these cases together?”
What is a graph database used for?
Graph databases are used in any setting where connected data needs to be explored, analysed, or operationalised. Common use cases include:
Fraud detection and financial crime
Graph databases help investigators trace transaction flows, identify mule accounts, surface hidden ownership structures, and detect suspicious patterns across customers, accounts, devices, and businesses.
Criminal intelligence and investigations
Investigators can connect data from case systems, phones, call records, vehicles, addresses, and intelligence reports to understand offender networks, uncover shared infrastructure, and build a more complete investigative picture.
Cybersecurity
Security teams use graph databases to connect users, devices, hosts, alerts, and events, enabling them to investigate attack paths, identify compromised assets, and understand how incidents spread across a network.
Knowledge graphs and connected enterprise data
Organisations use graph databases to create connected views of information spread across multiple systems, making it easier to search, reason over, and analyse complex data landscapes.
Recommendation and network analysis
Graph databases are also used in recommendation engines, social networks, supply chain analysis, and entity resolution, where the ability to traverse and analyse relationships quickly is critical.
Why graph databases matter for intelligence and investigations
In intelligence and investigative work, the challenge is rarely a complete lack of data. More often than not, the relevant information already exists, but it is spread across too many systems for anyone to quickly see the full picture.
A graph database helps bring that information together into a connected model that reflects how real-world activity unfolds. People are connected to phones, phones to messages, messages to locations, locations to events, and events to wider networks of activity.
This matters because many investigative questions are inherently relational:
- Who else is connected to this person of interest?
- Which devices, addresses, or accounts are shared across cases?
- How is this company connected to a sanctioned individual?
- Which people repeatedly appear near the same locations or events?
- What is the shortest path between this suspect and a known organised crime group?
These are difficult questions to answer when data is scattered across separate tables, spreadsheets, and systems. A graph database gives analysts a way to navigate those connections more naturally and quickly.
That does not mean the graph does the investigation for you. It means the underlying data model is better suited to the kinds of questions investigators actually need to ask.
How GraphAware Hume works with graph databases
GraphAware Hume is not a graph database itself. It is an investigation and intelligence analysis platform that helps organisations work more effectively with connected data.
GraphAware Hume uses graph technology to help analysts search, explore, visualise, and investigate complex networks of people, organisations, locations, events, and assets. It brings together graph visualisation, investigative workflows, AI-assisted capabilities, and analytical tools in a single environment designed for graph-powered intelligence analysis.
Using GraphAware Hume, organisations can:
- Integrate data from multiple structured and unstructured sources into a connected graph
- Visually explore relationships between people, devices, accounts, locations, and events
- Run link analysis and network analysis across investigative data
- Combine graph analysis with temporal and geospatial context
- Surface hidden patterns, supporting evidence, and investigative leads more quickly
- Support analysts with workflows designed for operational use, not just technical graph querying
In practice, that means GraphAware Hume helps analysts work with graph data in a way that is usable in real investigations, rather than expecting them to interact directly with the underlying database.
FAQs
Is Neo4j a graph database?
Yes. Neo4j is a graph database platform. More specifically, it is one of the best-known property graph databases.
It is worth separating the technology from the broader concept, though. A graph database is a category of database. Neo4j is one example of a product in that category.
What is the difference between a graph database and a knowledge graph?
A graph database is the underlying technology used to store and query connected data.
A knowledge graph is a connected representation of information about a domain, including entities, relationships, and often additional meaning such as types, rules, provenance, or business context.
In simple terms, a graph database is a type of database technology. A knowledge graph is the connected data model or layer of knowledge built on top of it.
What is the difference between a graph database and link analysis?
A graph database is a data model in which connected data can be stored and queried.
Link analysis is an analytical method that uses linked data to examine relationships among entities, identify patterns, and support investigations.
The two are related, but they are not the same thing. A graph database provides the underlying structure for connected data. Link analysis is one of the things analysts can do with that data.
Do graph databases replace relational databases?
Usually, no.
Relational databases are still the right choice for many operational systems and transactional workloads. Graph databases are most useful when the goal is to understand and analyse relationships across complex, connected datasets.
In many organisations, both are used together. Relational systems store operational records. Graph technology helps connect, enrich, and analyse those records in context.
Are graph databases only useful for big data?
No. Graph databases are useful when the problem’s structure is highly connected, not only when the data volume is large.
A modest dataset with rich relationships can still benefit from graph modelling if the key challenge is understanding how entities connect across people, organisations, events, assets, or transactions.