Network Diagrams: Examples, Best Practices, and How to Create

When you look at a standard bar chart or a line graph, you’re usually looking at how much of something exists or how a metric changes over time. These charts are excellent for answering questions about volume and trends. But what happens when you want to understand how things are connected? What if the most important story in your data isn’t about amounts but about relationships?

This is where the network diagram comes in. It’s one of the most powerful and insightful tools in data visualization. Network diagrams move beyond simple comparisons to reveal the complex web of relationships, connections, and interactions between different entities. 

Whether you’re mapping out social circles, analyzing IT infrastructure, or tracing how information flows through a company, a network diagram is often the best tool for the job. You’re in luck, because we are about to break it all down for you.

In this comprehensive guide, we’re going to explore everything you should know about network diagrams. You’ll learn what they are, the mechanics behind how they work, the best times to use them, and the design best practices that turn a messy web of data into clear, actionable insights. We’ll also walk you through how to create them and answer the most frequently asked questions.

Let’s dive in and start making connections.

What is a network diagram?

At its most basic level, a network diagram is a visualization that represents entities and the relationships between them. While other charts might focus on quantities, time periods, or categories, network diagrams focus entirely on connections.

To understand a network diagram, there are two fundamental components to understand:

• Nodes: These are the circles, icons, or points that represent the entities in your data set. A node could be a person, a computer server, a bank account, a specific product, or even a geographic location.
• Edges: These are the lines that connect the nodes. An edge represents the relationship, interaction, or flow between two entities.

You might see these charts referred to as node–link diagrams, graph visualizations, or network graphs. Regardless of the name, the central concept remains the same: to show structure, clustering, and interdependencies that are invisible in traditional row-and-column data formats.

The difference between network diagrams and other charts

It’s easy to confuse a network diagram with other types of flow-based charts, but there are distinct differences that you should know.

Network diagrams vs flowcharts

‍A flowchart represents a process or a sequence of steps, usually with a strict start and end point. It’s linear and procedural. A network diagram, on the other hand, represents relationships. It often has no specific beginning or end and can be cyclic or messy.

Network diagrams vs Sankey diagrams

‍A Sankey diagram focuses on the flow volume or magnitude between different states. While it shows connections, the width of the lines representing volume is the main story. In a network diagram, the existence of the connection itself is the primary story, although lines can be weighted to show strength.

Network diagrams vs org charts

‍An organizational chart is actually a specific type of network diagram known as a tree. However, it’s strictly hierarchical, meaning every child node has exactly one parent. General network diagrams can be non-hierarchical, meaning nodes can connect to anyone, creating complex webs rather than neat pyramids.

When (and why) to use a network diagram

Network diagrams aren’t your everyday chart. You wouldn’t use them to show monthly sales totals or website traffic trends. However, when your data involves complex relationships, they’re irreplaceable.

Here are some of the most common use cases where network diagrams shine.

Social networks and relationship mapping

This is perhaps the most famous application of the chart. Whether it’s analyzing followers on social media, mapping professional connections, or even visualizing terrorist cells in intelligence work, network diagrams allow you to see who knows whom. They help analysts identify key influencers (hubs), bridge-builders who connect different groups, and isolated individuals who might be on the periphery of a community.

IT infrastructure and system dependencies

Modern IT environments are incredibly complex. A network diagram can map out servers, databases, load balancers, and applications to show how they communicate. If one server goes down, a network diagram allows an engineer to immediately see which other systems will be affected. This is crucial for root cause analysis and impact assessment in cybersecurity and network administration.

Data lineage and data relationships

For data engineers and analysts, understanding where data comes from is vital. Network diagrams can visualize data lineage, showing how data flows from a source database through various transformation scripts and finally into a dashboard. This helps teams understand upstream and downstream dependencies, ensuring that a change in one table doesn’t break a report further down the line.

Communication patterns and interactions

You can use these diagrams to analyze internal communication within a company. Who emails whom? Which departments collaborate the most? This analysis can reveal silos within an organization or highlight informal leaders who connect different teams despite not having a high-ranking job title.

Knowledge graphs

Search engines and AI tools use knowledge graphs to understand how concepts relate to one another. For example, connecting “Leonardo da Vinci” to “Mona Lisa” and “Renaissance” creates a web of context. Network diagrams visualize these semantic relationships, helping us navigate complex information landscapes.

The benefits of using network diagrams

Why go through the effort of building these charts?

• They reveal structure: You can instantly see communities, clusters, and groups that aren’t obvious in a spreadsheet.
• They highlight importance: By looking at the number of connections or the position of a node, you can spot the most critical entities in a system.
• They support exploration: These charts are often interactive, inviting you to explore the data by clicking nodes and following connections.

When not to use a network diagram

Despite their power, they aren’t a silver bullet. You should avoid them when:

• Order or timeline is critical: If the sequence of events matters more than the relationship, use a timeline or flowchart.
• Exact quantities are the goal: If you’re comparing exact sales figures, a bar chart is far superior.
• The data set is too large: If you have thousands of nodes and edges, the chart can become an unreadable mess, often called a “hairball,” where no distinct patterns are visible.

How network diagrams work and mechanics

To create effective diagrams, it helps to understand the mechanics under the hood. It’s more than just drawing lines and about defining the nature of those lines and how a tool decides where to put them.

Nodes and edges explained

As mentioned, nodes are the “things” and edges are the “relationships.” However, these can be heavily customized to encode more data. Nodes can change size based on a metric like revenue or population, or change color based on a category like department or country. Edges can also vary in thickness to represent the strength of the relationship, such as the number of emails exchanged.

Directed vs undirected networks

• Undirected: The relationship is mutual. For example, Facebook friendships are usually undirected. If I’m your friend, you’re mine and the line connecting us has no arrow.
• Directed: The relationship flows one way. For example, on Twitter (X), you can follow someone without them following you back. In a data lineage map, data flows from a source to a target. These lines have arrows indicating the direction of the flow.

Weighted vs unweighted connections

• Unweighted: A connection simply exists. It’s binary. Either there is a line, or there is not.
• Weighted: The connection has a value. For example, in a flight network, the line between New York and London could be weighted by the number of flights per day. Thicker lines usually indicate higher weight, helping you distinguish strong connections from weak ones.

Layout algorithms and why layout choice matters

The most challenging part of a network diagram is figuring out where to place the dots. Unlike a scatter plot where X and Y coordinates are fixed by the data values, network nodes usually don’t have inherent coordinates.

Tools use algorithms to position nodes. The most common is the “force-directed” algorithm. Imagine the edges are springs pulling connected nodes together, while the nodes themselves are magnets pushing each other apart. The algorithm simulates this physics experiment, running until the diagram settles into a stable shape. This minimizes crossed lines and allows natural clusters to emerge visually. Choosing the right layout is critical because a bad layout can make a simple network look like a chaotic mess.

Types and variants

Depending on your data and your goal, you might choose a specific variant of the network diagram. Let’s look at a few.

Undirected network diagrams

These are the standard “spider webs” used for social connections or product affinities (for example, “people who bought X also bought Y”). They’re great for finding clusters and measuring centrality.

Directed network diagrams

Use these when flow or hierarchy matters. They’re essential for supply chain mapping, money flow analysis, or IT network traffic visualization where the “to” and “from” distinction is critical.

Weighted network diagrams

These are used when the strength of the relationship is just as important as the relationship itself. If you’re analyzing trade between countries, a weighted network allows you to see not just who trades with whom, but who the major trade partners are based on dollar value.

^{Hierarchical or layered networks}

If your data has a natural hierarchy like a family tree or corporate structure, a layered layout is best. This forces the root nodes to the top or left, with children flowing downward or rightward. It brings order to the chaos and makes the structure easier to scan.

Force-directed layouts vs radial layouts vs grid layouts

• Force-directed: The organic, clustered look. Good for finding natural groups and communities.
• Radial: Arranges nodes in a circle. Good for seeing connections between everyone in a group without the middle getting cluttered.
• Grid: Forces nodes into a strict grid. Useful when you have specific coordinates or require a very compact display, though it often obscures the natural structure of the network.

Design best practices and pitfalls

Creating a network diagram is easy. Creating a good one is hard. Because they can easily become cluttered, design choices are critical to success. Here are some tips to guide you.

Limit node count or use filtering

The golden rule of network diagrams is simplicity. If you try to visualize ten thousand nodes at once, you’ll end up with a solid block of color that tells you nothing. Use filtering to show only the most important nodes or allow people to drill down into specific clusters. If you have a large data set, consider aggregating nodes into groups before visualizing them.

Use color and size to encode meaning

Don’t use color just for decoration. Use it to represent categories (for example, blue nodes are servers, red nodes are databases). Use node size to represent importance (for example, the size of the node reflects the number of connections it has). This adds a layer of data density without adding clutter.

Avoid overlapping edges

While algorithms help, you might have to manually adjust the layout to reduce “edge crossing.” The more lines that cross each other, the harder the chart is to read. Try to adjust the settings of your layout algorithm to spread nodes apart further.

Label selectively

You probably can’t label every single node without making the text unreadable. Use dynamic labeling that only reveals the text when someone hovers over a node, or only label the major hubs that act as landmarks in the map.

Use interactivity for large networks

Static network diagrams are limited. Interactive ones allow people to zoom, pan, click to highlight neighbors, and filter the view. This interactivity turns a confusing chart into an exploratory tool.

Common pitfalls

• The hairball effect: Putting too much data on the screen at once.
• Unclear directionality: Using undirected lines when the relationship actually flows one way.
• Overuse of color: Using a rainbow of colors that confuses the reader rather than clarifying the groups. Stick to a limited palette.

Examples and storytelling tips

Data storytelling is about guiding the audience to the insight. Network diagrams can be overwhelming, so curating the experience for your viewers is important.

Example: social network showing influencer hubs

Imagine looking at a diagram of Twitter conversations about a specific hashtag. You see a few massive nodes in the center with thousands of lines radiating out. Those are your influencers. Surrounding them are smaller clusters of people talking among themselves. This tells a story of how information spreads from a central source to the masses.

Example: system architecture showing service dependencies

In an IT diagram, you might color-code nodes by “health status.” Suddenly, a red node in the center of the map draws your eye. You can instantly see that this failing server is connected to five critical applications. The story is immediate: “Fix this node or these five apps will fail.”

Example: data lineage showing upstream and downstream tables

A data analyst is looking at a report that seems broken. By pulling up a lineage network diagram, they can trace the line backward from the report to the source tables. They might spot that a specific transformation script (an edge in the graph) failed last night.

Storytelling tips

• Start with the hubs: When presenting, point out the biggest nodes first. They anchor the viewer.
• Explain clusters: Before diving into individual lines, explain why a group of nodes is clustered together. “This cluster on the left represents our marketing department.”
• Guide the reader: Don’t just show the whole map. Zoom in on a specific pathway to show a relevant journey or connection.

How to create a network diagram

Ready to build your own? The process starts with your data.

Data preparation

Unlike a standard spreadsheet where you just have rows and columns, network diagrams usually require two specific data sets to function correctly:

1. Node list: A list of all unique entities. It usually includes an ID and a Label (for example, ID: 1, Label: “John”). You can add other attributes here, like “Role” or “Department.”
2. Edge list: A list of connections using the IDs. It specifies “Source” and “Target” (for example, Source: 1, Target: 2).

You can also add attributes to these lists. For the node list, you might add a “Category” column. For the edge list, you might add a “Weight” column to define the thickness of the line.

Creating in common tools

Most modern Business Intelligence (BI) platforms and visualization tools have built-in network visualization capabilities or plugins. The general workflow is:

1. Assign nodes: You’ll drag your “Source” and “Target” fields into the visualization settings.
2. Choose layout: Select a force-directed layout for general analysis or a hierarchical layout for structured data.
3. Style: Map your metrics to the color and size options. For example, drag “Sales” to the node size property.

Creating in Excel

Let’s be clear here: Excel isn’t designed for network diagrams. It’s a grid-based tool, not a canvas-based tool.

However, if you absolutely must use Excel, you have two primary options.

• The manual way: Use the “Shapes” tool to draw circles and lines by hand. This is only feasible for tiny networks (for example, less than twenty nodes) and static presentations.
• Add-ins: There are third-party add-ins you can install that will generate a network diagram from an Excel table. These can be helpful for quick visualizations without leaving the spreadsheet environment.

Generally, if you’re serious about network analysis, you’ll want to move beyond Excel to a dedicated visualization tool that can handle the layout algorithms automatically.

Advanced tools

For heavy-duty analysis involving millions of nodes, data scientists turn to graph databases or coding libraries in Python or JavaScript. These tools allow for complex calculations, such as calculating the “betweenness centrality” (how much a node acts as a bridge) or detecting communities algorithmically. These tools are necessary when you’re moving beyond simple visualization and into the realm of deep network analytics.

Limitations and when to use an alternative

We love network diagrams, but they have limits.

Scalability challenges
They simply don’t scale well visually. Once you pass a few hundred nodes, readability drops off a cliff unless you have sophisticated zooming and filtering interaction. A static image of a thousand nodes is rarely useful.

Difficulty communicating exact values
It’s very hard for the human eye to judge the precise difference in size between two circles or the thickness of two lines. If you want to communicate that “Revenue A is exactly 5 percent higher than Revenue B,” a network diagram will fail.

Alternatives to consider

• Sankey diagrams: Use these if you want to show flow magnitude (for example, budget allocation) rather than just connectivity.
• Tree diagrams: Use these for strict hierarchies where every child has only one parent, such as an org chart or a file directory.
• Tables: Sometimes, a simple list of “Who is connected to whom” is more readable than a visual map, especially if you’re just looking up specific connections.

Conclusion and key takeaways

Network diagrams are a fascinating way to look at your data. They break us out of the row-and-column mindset and help us see the ecosystem of connections that drive our world. Whether you’re optimizing a supply chain or mapping a social community, these visuals provide context that other charts simply can’t match.

Key takeaways:

• Focus on relationships: Use network diagrams when the connection is more important than the specific value.
• Clean your data: Ensure you have a clean list of nodes and edges before you start. The quality of the diagram depends entirely on the quality of the link data.
• Design for clarity: Avoid the “hairball.” Filter your data and use color and size strategically to tell a story.
• Choose the right layout: The way you arrange the nodes changes how people perceive the data. Don’t settle for the default if it doesn’t make sense.

So, the next time you’re faced with a complex web of data, don’t just put it in a table. Map it out. You might be surprised by what you find connected in the chaos.