Graph visualization: an in-depth guide

Here you’ll discover everything you need to know about graph visualization, with links to articles by experts, white papers, tutorials and more.

What is graph visualization?

a visualization of a mafia network

Graph visualization, also called link analysis or network visualization, is a way to visually represent connections between entities in data.

The most effective way to visualize graphs is through a node-link model, where nodes represent entities and links represent connections.

These nodes and links can be anything – transactions between accounts, devices on a network, or phone calls between friends.

It doesn’t matter how large or small your data is, what it contains or where it’s stored. As long as there are connections you need to understand, you’ll find value in visualizing graphs.

Beginners guide to graph visualization
FREE: The ultimate guide to graph visualization

Proven strategies for building successful graph visualization applications


Why visualize graphs?

The world is densely connected. Often, to answer important questions, you need to understand those connections.

That’s where a graph visualization application can help. There are four key reasons behind its popularity:

  • Intuitive – The node-link graph model makes sense instantly, even to people who’ve never worked with graphs before.
  • Fast – Humans are great at spotting patterns when data is presented in a tangible format. Identifying trends and outliers is quicker when you can visualize them.
  • Scalable – data visualization lets analysts see beyond individual data points, revealing context, structure and individual connections.
  • Insightful – truly interactive visualizations let analysts engage and explore connected data, unlocking previously buried insights.

Read more about the benefits of a graph visualization application

social network analysis centrality measures

Getting started with graph visualization

For end-users, interacting with visual graphs is an intuitive and liberating experience. Going beyond tabular and aggregated views unlocks all kinds of analysis opportunities that would otherwise be impossible.

But the teams responsible for creating visualization tools often face a steep learning curve.

There are fundamental concepts to understand, and best practices to follow to make sure your application is as effective and insightful as possible. We’ve summarized them into a three-part, non-technical, introduction to the world of graphs.

Part 1: the basics of graphs

Understand the node-link graph model, and whether it’s the right approach for your data visualization project. The node-link graph model

Part 2: design best practices

When you’re planning a visualization approach, you’ll need to consider how your users will interact with their data. Designing your best app

Part 3: creating a visual graph model

How do you transform flat, tabular data into an intuitive graph model that will work for all your users? Building the visual graph model

Recommended viewing

This webinar, led by Corey Lanum, author of Visualizing Graph Data by Manning Publications, covers everything you need to start a successful graph data visualization project.

Uncover insight, understand threat
Follow the beginner’s guide with a full video transcript

Who needs graph visualization?

Data visualization plays a mission-critical role in all kinds of use cases and many different industries.

Starting with early pioneers in banking and law enforcement, visualizing graphs has exploded in popularity over the last 10 years.


Fraud management

Visualize unusual activities and connections to identify, investigate and predict any type of fraud – from identity theft to financial crime.

cyber security

Cyber security

Understand cyber threats, reveal network vulnerabilities, detect malware and discover trends using visualization techniques.

law enforcement

Law enforcement

Use data visualization to uncover threats and critical intelligence to help make the world safer.

Explore our top 12 Graph visualization use cases

example graph visualizer

JavaScript tools and tutorials

When it comes to building your own graph visualizer, you have options.

There are standalone tools, available off-the-shelf. Or community-built open source code libraries. Or you can create something in-house from scratch. Or, if you want the best of all worlds, you can choose a commercial SDK.

Our commercial SDKs are market leaders. They deliver the customizability and flexibility of in-house components, with the reliability, robustness, performance and advanced functionality of a technology backed by a dedicated team of experts.

KeyLines graph visualization toolkit

Graph visualization for JavaScript developers
Enjoy the flexibility to code how you like. Add data visualization to your applications that work anywhere, as part of any stack.

ReGraph graph visualization toolkit

Graph visualization for React developers
With ReGraph’s simple data-driven API, it’s quick and easy to add data visualizations to your React applications.

Using a different tech stack?

We’ve designed our data visualization SDKs to work seamlessly with any tech stack or data source. Here’s a selection of our most popular integration tutorials.

Graph visualization best practices

Beyond selecting your tools and tech stack, there are tips and tricks that’ll help make your project a success.

We’ve shared this best practice advice with hundreds of successful product teams over the past decade.

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.

Data visualization UX


4 easy styling options

Our SDKs let you create your own user experience. Here are 4 smart and simple styling options that take full advantage of that flexibility.

Interactive data visualization


Building accessible applications

Nearly 15% of people have a disability that could impact their ability to use software. Accessibility is essential.

Accessible data visualization

color design

Choosing the right colors

The brain instinctively accumulates and interprets certain cues from color. Use it to make visualizations that feel instantly familiar.

Color theory for data visualization

The data and visual model

graph data modeling

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?

This guide will help you decide

Big graph data visualization

5 steps

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.

Big graph data visualization

visualizing graphs at scale

Graph visualization at scale

The reality of visualizing huge volumes of graph data is messy, noisy, laggy charts. This post outlines two potential strategies.

Graph visualization at scale

Common challenges

Hairballs, snowstorms and starbursts can be a problem for many projects, obscuring data and preventing users from finding the insight they need. But we have solutions – click the images below to learn more.




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