Graph visualization: why it matters

Graph visualization, or network visualization, is a way of visually representing a certain type of data, known as graph data. It's an important tool for businesses and organizations exploring large amounts of complex data. This article describes what graph visualization is, what the major benefits are, and how graph visualization can be applied to different use cases across industries.

Graph visualization is the visual representation of data - nodes and edges - stored as a graph. To better understand graph visualization, let’s start with the basics.

The first step to understanding graph visualization is understanding what a graph is. Also called network, a graph is a collection of nodes (or vertices) and edges - also called links or relationships. Each node represents a single data point such as a person, a phone number, or a transaction. Each edge represents how two nodes are connected: a person possesses a phone number for example. 

Graph data is stored in a graph database such as Neo4j and Azure Cosmos DB. 

Graph analytics provides algorithms that help data scientists and data-driven analysts answer questions or make predictions. This way of representing data is well suited for scenarios involving connections and networks of entities, like social networks, telecommunication networks, protein interactions, and much more.

Graph visualization is when the nodes and edges of a graph are displayed in a visual way. Dedicated algorithms, called layouts, calculate the node positions and display the data on two (sometimes three) dimensional spaces. Some examples of layouts are force-directed where larger or more important elements are closer to the center, or radial layout, where nodes are arranged in concentric circles, showing dependencies.

These visualizations are data modeled as graphs. Any type of data asset that contains information about connections can be modeled and visualized as a graph, even data initially stored in a tabular way. For instance, the data from our example above could be extracted from a simple spreadsheet as depicted below.

The data could also be stored in a relational database or in a graph database, a system optimized for the storage and analysis of complex and connected data.

In the end, graph visualization is a way to better understand and manipulate connected data. And it offers several advantages.

Interactive visualization tools are an essential layer for organizations using graph technology to identify insights and generate value from connected data. There are a number of reasons why graph visualization is useful.

You will spend less time assimilating information because the human brain processes visual information much faster than written information. Visually displaying data ensures a faster comprehension which, in the end, reduces the time it takes to make decisions and take action.

You have a higher chance of discovering insights when interacting with data. Graph visualization tools offer the possibility to manipulate the data. It encourages data appropriation and  increases the possibility of discovering actionable insights. A study showed that managers who use visual data discovery tools are 28% more likely to find information in less time, than those who rely solely on managed reporting and dashboards (1).

You can achieve a better understanding of a problem by visualizing patterns and context. Graph visualization tools are perfect for visualizing relationships but also for understanding the context of the data. You get a complete overview of how everything is connected which allows you to identify trends, relationships, and correlations in your data.

It’s an effective form of communication. Visual representations offer a more intuitive way to understand the data and are an impactful medium to share your findings with decision-makers.

Everybody can work with graph visualization; it’s not limited to technical users or developers. More users can access the insights since specific programming skills are not required to interact with graph visualizations. This increases the value creation potential.

Let’s illustrate some of these benefits with a very simple example. We have sample data for eleven individuals with information about who works with who. Below is the same data sample in two formats: a table and a graph visualization.

In the second format, we’ve modeled the relationships between persons as edges to obtain a graph.

While in the first table it’s pretty hard to understand how the people in the data set work together, we get a clearer view in the graph visualization. We are able to distinguish two groups and an individual who seems to be the link between them, a pattern that we did not notice at first in the table.

Many industries are using graph technology to get more value from their connected data and reach their goals. Their common point, however, is the need to find connections or understand dependencies within their data. Here are a few examples of graph visualization use cases and the kinds of organizations using this technology.

Banks, insurance companies, and financial institutions are all up against increasingly sophisticated fraud schemes. From money laundering to insurance fraud to bank fraud, each of these organizations is required by compliance policy to detect fraud schemes, no matter how complex. Their data often combines customer information, claims details, financial records, politically exposed persons (PEPs) and sanctioned individuals or organizations. For these organizations, graph visualization is an efficient way to detect suspicious connections or patterns. It’s also an intuitive way to investigate fraud rings and criminal networks.

Organizations need to protect themselves from threats like zero-day vulnerabilities and DDoS or phishing attacks. Cybersecurity teams are now common in many large organizations including financial institutions, government agencies, and more. They collect data from servers, routers or application logs and network status in order to detect suspicious activity. Graph visualization is a powerful tool to digest this data and detect suspicious patterns at a glance. Being able to visually explore connections makes the finding of compromised elements easier and more time efficient.

Almost every government has an intelligence agency. To support law enforcement, national security or military objectives, these organizations collect and analyze data from various sources. The detection and identification of terrorist networks, for instance, has become a crucial objective in the past decades. Graph visualization enables intelligence analysts to see and explore connections between people, emails, transactions, phone records, and more, significantly accelerating investigations and making it easier to spot suspicious activity.

The field of IT operations management keeps growing with our increasing reliance on computer systems, networks and the growth of the Internet of Things. But as infrastructures become more complex, managing networks is often a challenge. Graph visualization allows IT managers to visualize dependencies between their assets (servers, switches, routers, applications, etc). It’s an intuitive way to perform impact or root cause analysis.

Numerous mature organizations implement enterprise architecture management. It consists of synchronizing business and IT data. The goal is to analyze, plan, and transform the business processes, applications, data, and infrastructure to maintain the organization's ability to change and innovate. With graph visualization, enterprise architects can visualize the organization’s assets and their dependencies. It helps to conduct impact analysis, obtain insights on the current situation and plan the right actions.

Protein interactions, drug compositions, disease networks: for life science data analysis, almost everything is about connections and dependencies. However, it can be challenging for researchers to identify insights and look for dependencies within large datasets. Graph visualization makes large amounts of data more accessible and easier to explore. It has many different applications, from linking drugs with adverse events and diseases with phenotypes to visualizing a network or understanding how diseases spread.

Linkurious Enterprise helps organizations extract insights from complex connected data. The software offers an easy-to-use graph visualization and analytics platform, accessible to both technical and non-technical users.

Used by teams of analysts and investigators, Linkurious Enterprise provides an intuitive UI to visually explore and investigate complex data from multiple sources. It facilitates the discovery of new insights and speeds up decision making.

Learn more and start getting more value from your connected data.

(1) https://www.tableau.com/sites/default/files/media/8604-ra-business-intelligence-analytics.pdf