What is graph visualization?
Graph visualization, also called link analysis or network visualization, is a way to visually represent, explore and analyze the connections between entities in data.
It helps analysts and investigators to understand patterns, relationships and structures within the data that would be difficult or impossible to uncover through other forms of analysis.
The node-link model is the most common and intuitive approach to graph visualization. In this model, nodes represent entities such as individuals, devices, or accounts, while links show the relationships between them, like transactions, interactions, or communications.
This node-link can represent any set of connections, from financial transactions between bank accounts to network traffic between devices or social interactions between friends.
It doesn’t matter how large or small your data is, or where it’s stored. As long as it contains relationships between different entities, graph visualization is a powerful tool for making sense of it.
Why visualize data as a graph?
The world is deeply interconnected, and to address crucial questions, it’s essential to grasp these connections. Graph visualization makes it easy to see, explore and understand complex relationships in data.
Here are four strengths that make graph visualization so effective:
It’s intuitive
Node-link graphs are instantly understandable, even to people who’ve never worked with connected data before. A network graph view gives a immediate and clear understanding of complex data relationships.
It’s fast
Humans are great at spotting patterns when data is presented visually. Graph visualization accelerates the process of identifying trends, outliers and clusters.
It’s holistic
Graph visualization makes it possible to connect data points from multiple and diverse sources, breaking down silos that would otherwise prevent a holistic view of the data in its full context.
It’s scalable
Graph visualization lets analysts see beyond individual data points, revealing the wider context, structure and relationships within data.
Unlike other forms of data analysis, graph data visualization treats connections with as much importance as the data points themselves. That makes it easier to uncover the hidden connections, patterns and anomalies that are not visible in aggregated analysis.
Want to learn more? Discover more benefits of graph visualization.
Graph visualization use cases
Graph visualization plays a mission-critical role in all kinds of use cases and many different industries. Some important use cases include:
Intelligence & Law enforcement
Graph visualization plays a vital role in law enforcement, security and intelligence throughout the intelligence cycle, from collection and analysis to dissemination.
It helps investigators explore communication records for lawful interception, turn open source data into valuable open source intelligence (OSINT) and generally ensure joined-up, effective investigations.
By visualizing critical links, graph visualization powers efficient analysis and clear insight dissemination, so officers and analysts can make faster, more informed decisions.
Financial Crime
In financial crime management, graph visualization turns complex data into actionable insights.
By mapping relationships across transactions and entities, graph visualization helps risk teams identify fraud, prevent money laundering and stop illicit activities.
Effective graph visualization streamlines routine checks and enhances investigations into complex, high-risk cases. By revealing suspicious patterns and connections between individuals or organizations, teams can better detect risks, investigate threats and ensure regulatory compliance.
Cyber Security
Graph visualization is a powerful tool in cloud and cyber security, helping teams understand cyber threats, map complex network topologies and protect their attack surfaces.
By visualizing connections between devices, users, systems and threats, it uncovers vulnerabilities and inefficiencies, aiding proactive threat detection, response and prevention cycles.
In short, graphs help security teams see and understand cyber threats, assess their risk exposure and mitigate potential breaches quickly.
Can’t see your use case here? Check out our use cases page.
Getting started with graph visualization
For end-users, visually interacting with graph data is intuitive and empowering. Moving beyond tabular and aggregated views opens up analysis opportunities that would otherwise be impossible.
But for the teams responsible for creating visualization tools, there’s a steep learning curve.
To ensure a seamless user experience, it’s essential to learn a few fundamental graph data visualization concepts and best practices. We’ve summarized these in our guide,
Some graph visualization best practices
Effective graph visualization is essential for making complex data more comprehensible and accessible. Following best practices in graph visualization ensures that insights are not only clear but also impactful.
We have a library of graph visualization guides to get you started:
Designing the user experience
How to avoid wrecking your graph app
The best data in the world is useless if your users can’t access it. A carefully designed, intuitive user experience (UX) makes all the difference.
Graph data modeling 101
How do you transform flat, tabular data into an intuitive graph model that works for all your users? What becomes a node, a link, or an attribute?
Building accessible applications
Nearly 15% of people have a disability that could impact their ability to use software. Ensuring they have equal access to technology is essential.
Choosing the right colors
The brain instinctively accumulates and interprets certain cues from color. Use it to make visualizations that feel instantly familiar.
Visualizing very large graph datasets
Five steps to tackle big graph data visualization
How do you turn millions (or billions) of nodes into something your users will understand? The data visualization funnel holds the answers.
Graph visualization at scale
The reality of visualizing huge volumes of graph data is messy, noisy, laggy charts. This post outlines two potential strategies for visualizing large datasets at scale.
Graph visualization techniques
An introduction to graph layouts
Good automated graph layouts are a vital tool to detangle the most complex graph dataset.
Social network centrality measures
Social network centrality algorithms make it easy to identify important nodes in a network, based only on the connections between them.
Graph data analytics 101
Learn how to reveal the story behind your data with powerful graph analytics algorithms.
Visualizing time-based graphs
Networks are never static – they constantly change over time. Visualizing this evolution unlocks a new dimension of insight.
Want to try it for yourself?
Our software development kits make it quick and easy to build fully-customized graph visualization applications
Frequently asked questions about graph data visualization
What are alternatives to node-link graph visualization?
Node-link graph visualization is often the best choice for exploring connected data, but in some cases, alternative data visualization approaches can complement or even better suit your data. Here are a few we come across often:
Geospatial visualization
Map views can be an alternative to graph-based visualizations when you need to display connected data in its spatial context. These can range from simple connected location markers on a map, to more sophisticated geospatial graph visualization tools with a rich interactive user experience. Read more about geospatial visualization.
Timeline visualization
Timeline visualization is a powerful tool for representing data or events in a sequential order over time. It is particularly effective for displaying connections between events and entities over time, to see how events unfolded and uncover key moments. Read more about timeline visualization.
Heatmaps
Heatmaps use color gradients to represent values, making them useful for understanding density or intensity across a large dataset. They’re especially useful in situations where you want to highlight variations in data – for example, time periods of intense activity. Read more about heatmapping.
Chord diagrams
A chord diagram is a circular visualization that shows relationships between data categories using arcs and curved links. It’s useful for showing connections between a relatively small number of entities – such as trade flows or simple network structures. Read more about chord diagrams.
What are common challenges in graph visualization?
The connected nature of graph data can pose some tricky visualization challenges. Datasets with too many connections become ‘hairballs’. Too few connections leads to ‘snowstorms’. Networks with a single super-connected nodes create ‘starbursts’.
Happily, powerful graph visualization techniques and tools can help users solve them all. Click the images below to learn more:
Hairballs
Snowstorms
Starbursts
What is a graph visualization SDK?
A graph visualization SDK is a developer toolkit for building interactive, high-performance visualizations of connected data. It offers flexibility and scalability, helping developers create custom applications for exploring complex data.
If you need a graph data visualization solution, you have a few options:
- Off-the-shelf applications come with a pre-built interface and features. They offer a simple, one-size-fits-all approach, ideal for basic analysis tasks.
- Graph visualization SDKs let you design and build custom applications tailored to your users’ needs. This makes them easier to use and more effective for in-depth analysis.
- Open-source libraries provide a starting point for visualization but often require significant customization and engineering effort to match the performance, scalability, and ease of use of dedicated solutions.
What integrations are available with our graph visualization solutions?
We’ve designed our data visualization SDKs to work seamlessly with any tech stack. Whether you’re building a web app, integrating with existing analytics tools, or developing a custom solution, our flexible APIs ensure smooth implementation on frameworks including:
Our SDKs are also database agnostic. Whether you use a graph database, a relational or SQL database or no database at all, our tools integrate seamlessly. Here’s a selection of our most popular graph database integration tutorials.
Learn more about visualizing graph databases
Why choose our graph visualization software?
Our developer toolkits make it easy to build powerful, customized user experiences for complex data.
Alongside our expert guidance and technical support, you’ll have your visualization application ready in weeks, not months.
KeyLines
The graph visualization SDK for JavaScript developers
ReGraph
Hassle-free graph visualization for React developers
KronoGraph
Advanced timeline visualizations that scale