What is graph machine learning?
Graph machine learning (Graph ML) is a branch of machine learning that learns from connected data rather than treating each record in isolation.
Unlike traditional machine learning, which typically analyses rows in a table, graph machine learning considers both the properties of individual entities and the relationships between them.
This makes it particularly effective for problems where connections are as important as the entities themselves. In an intelligence analysis context, these problems include identifying suspicious behaviour, uncovering hidden relationships, and prioritising investigative effort across large, connected datasets.
Today, graph machine learning forms part of a broader AI ecosystem that also includes graph algorithms, large language models (LLMs), GraphRAG and agentic AI. While these technologies solve different problems, they often work together to help analysts explore, understand and reason over complex connected data.
Why relationships matter in machine learning
Most traditional machine learning models assume that every record is independent.
In reality, many real-world problems are highly connected.
- A bank account belongs to a customer
- A customer owns several businesses
- Those businesses share directors
- The directors communicate with known offenders
- Those offenders appear in previous investigations
Looking at individual bank accounts in isolation may reveal little. However, once those accounts are connected to company directors, beneficial owners, addresses, telephone numbers and previous investigations, new patterns emerge.
Graph machine learning learns from both the entities and the relationships between them, allowing models to recognise structures that traditional machine learning often overlooks.
Graph machine learning captures this context by learning not only from the attributes of entities but also from the structure of the network itself.
For intelligence analysts, this means AI systems can incorporate connections between entities when identifying patterns or making predictions.
Common graph machine learning tasks
Graph machine learning supports a variety of analytical tasks.
Node classification
Predicting the characteristics or category of an entity based on both its own attributes and the surrounding network.
For example:
- identifying potentially fraudulent companies
- classifying high-risk accounts
- prioritising persons of interest
Link prediction
Predicting relationships that may exist but have not yet been observed, for example previously unknown criminal associates, likely beneficial ownership, or hidden financial relationships.
These predictions provide analysts with investigative leads rather than definitive conclusions.
Community detection
Identifying groups of entities that appear more closely connected to one another than to the wider network.
This can help analysts understand organised crime groups, coordinated activity or hidden organisational structures.
Anomaly detection
Detecting entities or patterns that differ significantly from normal behaviour.
Examples include unusual transaction flows, unexpected communication patterns or accounts behaving differently from their peers.
Graph machine learning vs graph algorithms
Although often discussed together, graph machine learning and graph algorithms solve different problems.
Graph algorithms analyse the existing structure of a graph. For example, they can identify shortest paths, calculate centrality measures or detect communities using mathematical techniques.
Graph machine learning goes a step further. Rather than analysing the graph directly, it learns from examples in order to make predictions about unseen data.
In practice, modern intelligence platforms often combine both approaches. Graph algorithms help analysts understand what already exists, while graph machine learning helps identify what might exist or what deserves further investigation.
Graph machine learning vs GraphRAG
Graph machine learning and GraphRAG are complementary technologies that address different challenges.
Graph machine learning learns patterns from connected data to make predictions.
GraphRAG combines knowledge graphs with retrieval-augmented generation (RAG), enabling large language models to retrieve structured context before generating responses.
For example, a graph machine learning model might identify accounts that are likely to be involved in money laundering. A GraphRAG system might then help an analyst ask natural-language questions about those accounts, retrieve supporting evidence from the knowledge graph and generate an evidence-backed explanation.
Both approaches benefit from the same underlying connected data, but they perform different roles within an intelligence platform.
Learn more in GraphRAG vs Agentic Retrieval, a blog post from our Chief Scientist exploring an emerging agentic approach to retrieval-augmented generation.
Graph machine learning and large language models
The rapid adoption of large language models has transformed how organisations interact with data. Rather than replacing graph machine learning, LLMs complement it.
Graph machine learning excels at recognising structural patterns, making predictions and detecting anomalies across large connected datasets.
Large language models excel at understanding natural language, summarising information and assisting analysts through conversational interfaces.
Together, these technologies enable analysts to both discover new patterns and understand them more effectively.
Graph machine learning in intelligence analysis
Graph machine learning is rarely used in isolation. Within modern intelligence platforms, it complements traditional analytical techniques such as link analysis, timeline analysis and geospatial analysis.
For example, graph machine learning can help:
- prioritise entities that warrant further investigation
- identify suspicious communities within large networks
- suggest previously unknown relationships
- detect anomalous behaviour
- support risk scoring and investigative prioritisation
Importantly, these predictions should support analyst judgement rather than replace it.
Human analysts remain responsible for evaluating evidence, considering operational context and making investigative decisions.
Challenges of graph machine learning
Like all AI techniques, graph machine learning depends on the quality of the underlying data.
Common challenges include:
- incomplete or fragmented data
- duplicate entities
- changing network structures
- explainability
- maintaining provenance and evidential confidence
- avoiding bias in training data
For intelligence organisations, explainability is particularly important. Analysts need to understand why a model has highlighted an entity or suggested a relationship before acting upon its recommendations.
Graph machine learning software
Modern graph machine learning platforms combine graph databases, machine learning frameworks and knowledge graphs to analyse connected data at scale.
Increasingly, these capabilities are integrated with graph algorithms, GraphRAG, large language models and interactive investigation tools rather than existing as standalone systems.
GraphAware Hume follows this philosophy by combining graph-powered intelligence analysis with explainable AI, knowledge graphs and analyst-driven workflows. Rather than treating AI as a replacement for human expertise, Hume is designed to help analysts explore connected data, validate findings and make evidence-based decisions with greater confidence.
Frequently asked questions
What is graph machine learning?
Graph machine learning is a branch of machine learning that learns from connected data, using both entity attributes and relationships to make predictions.
Is graph machine learning the same as GraphRAG?
No. Graph machine learning learns patterns from graph-structured data to make predictions. GraphRAG uses knowledge graphs to provide structured context for large language models during retrieval and reasoning.
Does graph machine learning require a graph database?
Not necessarily, but graph databases provide a natural way to store, query and analyse connected data, making them well suited to graph machine learning workflows.
Is graph machine learning replacing graph algorithms?
No. The two approaches are complementary. Graph algorithms analyse existing relationships, while graph machine learning learns from those relationships to make predictions about new or unseen data.
How is graph machine learning used in intelligence analysis?
Graph machine learning can help identify suspicious patterns, detect anomalies, predict hidden relationships and prioritise investigative leads. These insights support analysts, who remain responsible for interpreting evidence and making operational decisions.