Network analysis software: features and benefits

What is network analysis software?

Network analysis software is a specialised tool used to analyse and visualise relationships between entities in large datasets. At its core, network analysis evaluates the connections between entities, allowing analysts to identify patterns, trends, and anomalies that might not be apparent through traditional analysis methods.

In network analysis, entities are represented as nodes, while the relationships between them are depicted as links or edges. These nodes can represent a wide range of entities such as people, organisations, events, or transactions.

The visual representation of data through networks is crucial, as it allows analysts to intuitively grasp complex relationships and uncover hidden connections.

How does network analysis software Work?

Network analysis software represents data as a network of nodes and edges, ready for analysts to explore as they search for hidden connections and complex relationships.

The process typically involves several steps:

• Data Ingestion: Collecting and importing data from various sources.
• Data Representation: Mapping entities to nodes and relationships to edges.
• Visualisation: Creating visual representations of the network to identify patterns.
• Analysis: Applying algorithms and queries to uncover insights.

Graph databases like Neo4j are often used in network analysis due to their ability to efficiently store and query complex interconnected data.

Graph-powered network analysis

Traditional network analysis typically relies on table-based data models. As relationships become more complex, these approaches can struggle with performance and make it difficult to explore connections beyond a few hops.

Graph technology addresses these limitations by storing relationships as first-class citizens. This enables efficient multi-hop traversal, flexible schema evolution, and straightforward integration of additional data sources.

Computational complexity

In real-world scenarios, network analysis often involves gigabytes of interconnected data. Traversing even a few levels using repeated table joins across large datasets can quickly become computationally expensive and memory-intensive.

GraphAware’s link analysis architecture uses native graph queries to traverse relationships directly, following multi-step connections as a single logical operation. Because relationships are stored explicitly, traversal performance is driven by the local connectivity of the graph rather than the size of the overall dataset, enabling consistent performance even as network complexity and depth increase.

Lack of persisted relationships

Analysts are frequently required to answer time-based questions, such as determining who owned an asset at a specific point in time. Supporting this requires temporal data that records when relationships started and ended.

In relational systems, where relationships are not stored as first-class entities, answering new temporal questions often means writing additional queries and reconstructing ownership chains through repeated joins.

In contrast, GraphAware’s link analysis software stores both nodes and relationships with their own properties, including attributes such as start and end dates. Introducing a temporal dimension typically requires only adding a time-based condition to the traversal query, rather than rebuilding the logic from scratch.

The graph model also enables flexible exploration. New data sources — such as transaction records or regulatory filings — can be incorporated into the existing graph structure without schema disruption. This allows investigators to uncover emerging patterns and connections as the network evolves.

Comparing traditional with graph-powered link analysis

Data ingestion

Effective network analysis begins with seamless data ingestion. To establish meaningful links between entities, data from various sources must be connected first. Tools like Hume Orchestra streamline the data ingestion process by enabling documentation and integration of multiple data streams, ensuring that all relevant information is available for analysis.

Graph visualization

Graph visualization is a cornerstone of network analysis software, allowing analysts to identify patterns and connections quickly. Visual interfaces transform complex data into intuitive graphs and charts, making it easier to detect relationships and anomalies.

Data integration and enrichment

Network analysis software excels in connecting disparate data sources, both structured and unstructured.

Structured data sources may include databases and spreadsheets, while unstructured sources encompass text documents, social media, and more. Data normalisation and resolving inconsistencies are critical steps, often achieved through entity resolution algorithms within graph databases.

Enriching data with external intelligence sources provides a comprehensive view of the analysed entities, creating a single source of truth. This capability ensures that all relevant information is considered, enhancing the accuracy and depth of the analysis.

Analytical capabilities

Advanced analytical capabilities are essential for deriving meaningful insights from network data. Key features include:

• Multihop connections and shortest path: Identify the shortest path between entities, revealing direct and indirect relationships that are impossible to detect with traditional software.
• Maps and time bars: Visual tools that display geospatial and temporal data, providing context to the relationships and events.
• Community detection algorithms: Identify clusters and communities within the network, such as organised crime groups.
• Centrality analysis: Determine the importance of nodes within the network, helping to identify key players.

These analytical tools enable the execution of complex queries across vast amounts of data, uncovering patterns and connections that drive actionable intelligence.

Reporting and collaboration

Effective reporting and collaboration features are vital for crime analysts working in teams. Network analysis software offers capabilities such as:

• Saving, sharing, and retrieving link charts: Easily share visualisations with team members for collaborative analysis.
• Automated alerting: Receive notifications for patterns of interest, ensuring timely responses to emerging threats.
• Report and dashboard generation: Create comprehensive reports and dashboards that summarise key findings and insights.

These features facilitate efficient teamwork and ensure that critical information is communicated effectively across the organisation.

Network analysis software provides significant advantages for crime analysts and forensic accountants. By structuring and visualising complex relationships across large datasets, it enables more efficient investigations, deeper pattern detection, and risk-informed analysis. Analysts can move beyond isolated records to uncover hidden connections and generate actionable intelligence.

GraphAware’s Hume is designed to integrate fragmented data into a unified, graph-native environment, with advanced analytical capabilities and purpose-built investigative workflows.

As data volumes and network complexity continue to grow, adopting graph-based analysis is becoming increasingly important for organisations tackling crime and financial fraud. For investigative teams seeking to modernise their analytical capabilities, GraphAware provides a scalable and intelligence-led approach to connected data.