Network Analysis

9 min read

What is Network analysis?

Network analysis is the process of studying the structure and behaviour of complex systems that can be represented as networks of interconnected entities. This article covers the basic concepts, techniques, and importance of network analysis in various domains, such as big data, intelligence analysis, fraud detection, and cyber security.

What are the techniques of network analysis?

Network analysis involves various techniques to measure, visualise, and model the properties and patterns of networks. Some of the common techniques are:

Network metrics

Network metrics: quantifying different aspects of a network using numerical values. For example, network metrics can tell us how large, dense, or diverse a network is, how far apart or close together the nodes are, how connected or isolated the nodes are, how well the network is organised into groups or modules, and how similar or different the nodes are in terms of their connections or attributes. Network metrics can help compare and classify networks, as well as identify important nodes and communities within them.

Network visualisation

Network visualisation: creating graphical representations of networks, using shapes, colours, and layouts to convey information about the network structure and attributes. For example, network visualisation can show us the overall shape and layout of the network, and distribution and variation of node sizes and colours. Network visualisation can help explore and communicate the features and insights of networks, as well as reveal hidden patterns and anomalies.

Network modelling

Network modelling: creating mathematical or computational models of networks, using rules, algorithms, and simulations to analyse, and predict network behaviour and dynamics. For example, network modelling can help us understand and explain the underlying mechanisms and processes of networks, such as how they change, how they respond to external stimuli, and how they influence or are influenced by other networks. Network modelling can help test hypotheses and scenarios, as well as design and optimise network systems.

What is network analysis used for? Which sectors use it? Who needs it?

Network analysis is used for various purposes, such as understanding, predicting, or optimising the structure and behaviour of complex systems that can be represented as networks. Network analysis is needed by anyone who wants to gain insights and knowledge from network data, or who wants to design and manage network systems. Some key examples of network analysis use cases are:

  • Criminal intelligence: Analysts and investigators use network analysis to identify and track criminals, gangs, and organisations that are involved in illicit activities, such as trafficking, smuggling, or terrorism. Network analysis can help reveal the structure and dynamics of criminal networks, such as their size, composition, hierarchy, or modus operandi, as well as the roles, behaviours, and outcomes of individuals and groups within them.

  • Banking: Bankers and regulators use network analysis to detect and prevent financial fraud, such as money laundering, tax evasion, or Ponzi schemes. Network analysis can help uncover the structure and function of financial networks, such as their transactions, flows, or balances, as well as the risks and opportunities of financial phenomena, such as contagion, stability, or efficiency.

  • Industry: Businesses and enterprises use network analysis to improve their products and services, such as enhancing quality, efficiency, or innovation. Network analysis is used in various domains, such as marketing, finance, engineering, or management, to optimise and leverage networks, such as customer networks, financial networks, supply networks, or organisational networks.

  • Government: Agencies and institutions use network analysis to support their policies and decisions, such as ensuring security, stability, or welfare. Network analysis is used in various areas, such as intelligence, defence, health, or education, to monitor and influence networks, such as criminal networks, terrorist networks, disease networks, or social networks.

What are the advantages of network analysis?

Network analysis has many advantages, such as:

  • Discovering hidden patterns and insights from complex and large-scale data. Networks can capture the richness and diversity of data, such as the relationships, attributes, and dynamics of entities, as well as the interactions, flows, and feedbacks of systems. Network analysis can help reveal the patterns and insights that are otherwise difficult to detect or understand using conventional methods, such as statistics, graphs, or tables.

  • Explaining and predicting the behaviour and outcomes of complex and dynamic systems. Networks can model the mechanisms and processes of systems, such as how they form, grow, change, or decay, how they respond to external stimuli or perturbations, how they influence or are influenced by other systems, and how they exhibit emergent phenomena or complex behaviour. Network analysis can help explain and predict the behaviour and outcomes of systems using mathematical or computational tools, such as metrics, visualisations, or simulations.

  • Optimising and influencing the structure and performance of complex and adaptive systems. Networks can represent the design and management of systems, such as their configuration, function, or evolution, as well as their objectives, constraints, or trade-offs. Network analysis can help optimise and influence the structure and performance of systems using analytical or strategic tools, such as optimization, control, or game theory.

What is the importance of network analysis for Big Data? Why is it important?

Network analysis is important for big data, because it can help us deal with the challenges and opportunities of Big Data. Big Data refers to the massive and diverse datasets that are generated at high speed from various sources. These datasets contain a wealth of information and insights that can be used for various purposes. However, they also pose difficulties for storing, processing, or analysing the data using conventional methods.

Network analysis can help us handle the volume, variety, and velocity of Big Data by using network structures and algorithms. Network structures, such as graphs, trees, or matrices, can efficiently store and manipulate sparse and heterogeneous data. Network algorithms, such as parallel, distributed, or streaming algorithms, can quickly and scalably process and analyse large and dynamic data.

Network analysis can also help us extract the value and knowledge from Big Data by using network techniques. Network techniques, such as metrics, visualisations, or models, can reveal the patterns and insights that are hidden or latent in the data. These patterns and insights can tell us about the relationships, attributes, and dynamics of entities and systems, as well as the interactions, flows, and feedbacks of systems.

How does network analysis help intelligence agencies and police?

Network analysis can help intelligence agencies and police to fight crime and terrorism by using network data, such as phone records, social media posts, or bank transactions, to identify and track suspects, networks, and activities. Some of the ways that network analysis can help intelligence agencies and police are:

Finding the key players in criminal networks

Finding the key players and leaders in criminal or terrorist networks. Network analysis can use centrality measures, such as degree, closeness, betweenness, or eigenvector, to rank the nodes in a network according to their importance, influence, or connectivity. This can help find the key players and leaders who have the most connections, the shortest paths, the highest control, or the highest prestige in the network, and who may be the most valuable targets or sources of information.

Finding the hidden or covert members and links in criminal or terrorist networks. Network analysis can use community detection methods, such as modularity, spectral, or hierarchical, to partition the network into groups or modules that have more connections within than between them. This can help find the hidden or covert members and links that belong to the same group or module, and who may be involved in the same activity or operation.

Finding the patterns in criminal networks

Finding the patterns and trends in criminal or terrorist networks. Network analysis can use temporal or dynamic methods, such as time series, event graphs, or state machines, to analyse the changes and evolution of the network over time. This can help find the patterns and trends in the network, such as the frequency, duration, or sequence of events, the emergence or disappearance of nodes or links, or the growth or decay of the network, and who may indicate the activity or intention of the network.

How do you detect fraud with network analysis?

Network analysis can help detect fraud with network data, such as financial transactions, phone calls, or emails, that can reveal the fraudulent activities, networks, and actors. Some of the ways that network analysis can help detect fraud are:

Anomaly detection methods

Using anomaly detection methods such as outliers, motifs, or subgraphs, to identify the nodes and links that deviate from the normal or expected behaviour or pattern of the network. These nodes and links may indicate fraudulent or malicious activities, such as money laundering, identity theft, or phishing.

Pattern mining methods

Using pattern mining methods such as frequent, sequential, or associative, to discover the schemes and patterns that are repeated or correlated in the network. These schemes and patterns may indicate fraudulent or malicious strategies, such as collusion, fraud rings, or fraud chains.

Causal inference methods

Using causal inference methods, such as Bayesian, Granger, or Pearl, to infer the causal or influential relationships and effects among the nodes and links in the network. These relationships and effects may indicate fraudulent or malicious causes or consequences, such as fraud triggers, fraud propagation, or fraud impact.

What is AML network analysis?

AML network analysis is the application of network analysis to anti-money laundering (AML), which is the process of preventing, detecting, and reporting the illegal use of money for criminal purposes, such as terrorism, drug trafficking, or tax evasion. AML network analysis can help identify and track the money laundering activities, networks, and actors, as well as comply with the AML regulations and standards.

AML network analysis can use network data, such as financial transactions, customer profiles, or suspicious activity reports, to construct and analyse the money laundering networks, such as the placement, layering, or integration stages, the source, destination, or intermediary nodes, or the amount, frequency, or complexity of transactions. AML network analysis can use network techniques, such as metrics, visualisations, or models, to measure, visualise, and model the properties and patterns of the money laundering networks, such as their size, density, centrality, modularity, or anomaly. AML network analysis can also use network tools, such as optimization, control, or game theory, to optimise and influence the structure and performance of the money laundering networks, such as their detection, prevention, or mitigation.

How does Graphaware help analyse networks? What are the examples of knowledge graph use cases?

Graphaware provides solutions for network analysis using knowledge graphs, which are networks of data that represent entities and their relationships using nodes, links, and properties, as well as rules, logic, and semantics. Graphaware can help analyse networks by using knowledge graph technologies, such as graph databases, graph algorithms, or graph analytics, to store, query, and process network data, as well as to generate insights and recommendations from network data.

Some of the examples of knowledge graph use cases that Graphaware can help with are:

  • Financial: Graphaware can help analyse financial networks, such as transactions, customers, or accounts, to detect and prevent fraud, money laundering, or tax evasion, as well as to optimise and personalise financial products and services, such as loans, investments, or insurance.

  • Criminal investigation: Graphaware can help analyse criminal networks, such as suspects, victims, or evidence, to identify and track criminals, gangs, or organisations, as well as to solve and prevent crimes, such as trafficking, smuggling, or terrorism.

  • Cyber security: Graphaware can help analyse cyber security networks, such as devices, computers, or networks, to detect and mitigate cyber threats, such as hackers, viruses, or worms, as well as to optimise and secure cyber systems, such as the internet, the web, or the cloud.