What is network analysis?
Network analysis is the process of examining relationships between people, organisations, devices, locations, events, or other connected entities to understand how they interact and influence one another.
Rather than analysing records in isolation, network analysis focuses on the connections between them. This makes it easier to identify influential individuals, hidden groups, indirect relationships, and patterns of activity that may otherwise remain undiscovered.
Network analysis is widely used in intelligence analysis, law enforcement, fraud detection, cybersecurity, financial crime investigations, and social network analysis to solve complex, relationship-driven problems.
Why network analysis matters
Many real-world problems involve large networks of interconnected people, organisations, assets, and events.
Traditional databases and spreadsheets are effective for storing information, but make it difficult to understand how entities are connected. As networks grow in size and complexity, manually tracing relationships becomes increasingly time-consuming.
Network analysis helps organisations:
- Identify hidden relationships
- Understand how information or activity spreads
- Detect organised groups and communities
- Find influential individuals within a network
- Discover unusual patterns and anomalies
- Prioritise investigative leads
By understanding both direct and indirect relationships, analysts can develop a more complete picture of complex situations.
How network analysis works
Network analysis represents data as a graph consisting of:
- Nodes: representing entities such as people, organisations, accounts, locations, or devices.
- Relationships (edges): representing the connections between those entities, such as ownership, communication, transactions, employment, or shared activity.
Once data has been connected, analysts can explore networks visually or apply graph analytics to answer investigative questions.
Common analytical techniques include:
- Link analysis to explore direct and indirect relationships.
- Centrality analysis to identify influential or highly connected entities.
- Community detection to identify groups of closely connected entities.
- Path analysis to discover how two entities are connected.
- Similarity analysis to identify entities with shared characteristics or behaviours.
These techniques help reveal patterns that are often difficult to detect using traditional analytical approaches.
Common applications of network analysis
Intelligence and law enforcement
Investigators use network analysis to understand criminal organisations, identify associates, reconstruct communication networks, and uncover hidden relationships among suspects, victims, and locations.
Financial crime and fraud detection
Financial institutions analyse transaction networks to identify money laundering, organised fraud, shell companies, and suspicious financial relationships.
Cybersecurity
Security teams use network analysis to investigate attack paths, understand lateral movement, identify compromised systems, and uncover relationships between threat actors, infrastructure, and malware.
Supply chain risk
Organisations analyse supplier networks to identify dependencies, hidden ownership structures, and potential operational risks across complex supply chains.
Social network analysis
Researchers and organisations use network analysis to understand communities, identify influential individuals, measure information flow, and analyse patterns of interaction across social platforms.
Why graph technology is ideal for network analysis
Graphs provide a natural way to represent connected data.
Unlike traditional relational databases, which require complex joins to analyse relationships, graph technology stores relationships as first-class citizens. This makes it much faster and easier to explore highly connected datasets.
Knowledge graphs allow analysts to connect data from multiple sources into a single, contextual view, making network analysis more efficient and more intuitive.
By combining graph technology with graph analytics, organisations can uncover hidden patterns, investigate complex relationships, and generate insights that would otherwise remain difficult to discover.
Network analysis with GraphAware Hume
GraphAware Hume combines graph visualisation, graph analytics, and investigative workflows within a single environment for graph-powered intelligence analysis.
Using GraphAware Hume, analysts can:
- Explore complex networks visually
- Perform link analysis across multiple data sources
- Investigate relationships between people, organisations, locations, and events
- Apply graph analytics to uncover hidden patterns
- Analyse temporal and geospatial context alongside network relationships
- Collaborate on investigations within a shared intelligence environment
By transforming fragmented information into connected intelligence, GraphAware Hume helps analysts investigate complex networks more efficiently and make better-informed decisions.
FAQs
What is network analysis?
Network analysis is the process of analysing relationships between connected entities to understand how people, organisations, events, or assets interact within a network.
What is the difference between network analysis and link analysis?
Link analysis focuses on exploring relationships between entities within a network. Network analysis is a broader discipline that includes link analysis alongside graph analytics, community detection, centrality analysis, path analysis, and other techniques used to understand connected data.
What is network analysis used for?
Network analysis is commonly used in intelligence analysis, law enforcement, fraud detection, cybersecurity, financial crime investigations, supply chain risk analysis, and social network analysis.
Why are graphs useful for network analysis?
Graphs naturally represent relationships between entities, making it easier to analyse highly connected data, identify hidden patterns, and investigate complex networks that would be difficult to understand using traditional databases.
What are examples of network analysis?
Examples include identifying criminal networks, tracing money-laundering activity, detecting fraud rings, investigating cyberattacks, analysing supply chains, and understanding social media communities.