In upcoming articles, we’ll be talking about the importance of “network density” as a measure of network health and effectiveness. I think network density has important ramifications for the way business works and for making the world a better place.

To better understand what it is, this article will show you how to easily calculate network density. The goal isn’t to get you calculating the network density of your Facebook connections – although you probably *could* if you wanted. No, the idea is to take just a few minutes to understand this easy calculation, as a way to give you a more intuitive feel for what network density is. With that, you’ll be better positioned to actually apply this important concept in your work.

## What is Network Density?

First a few quick definitions. In a network, the things that are connected are usually called *“nodes.”* A node might be a person, a computer, or even some hyperlinked text. The connections between nodes are sometimes called *“connections”* and sometimes called *“edges”* – but it’s really just the same thing.

“Network density” describes the portion of the *potential* connections in a network that are *actual* connections. A *“potential connection”* is a connection that could potentially exist between two “nodes” – regardless of whether or not it actually does. This person *could* know that person; this computer *could* connect to that one. Whether or not they *do* connect is irrelevant when you’re talking about a potential connection. By contrast, an *“actual connection”* is one that actually exists. This person *does* know that person; this computer *is* connected to that one.

A couple of examples might help. At a family reunion, the *actual* connections between people are quite numerous – it may even be a hundred percent of all the potential relationships in the room. In contrast, the *actual* connections between people on a public bus – the number of people who actually *know* each other – is likely to be quite low relative to all the *potential* relationships there.

A family reunion has high network density, but a public bus has low network density.

## Calculating Network Density:

So, here’s how you calculate network density. In the below chart, “PC” is “Potential Connection” and “n” is the number of nodes in the network. Don’t let the numbers turn you off; they’re actually pretty straightforward:

In the above chart, examples “A” and “B” illustrate cases where the number of *actual connections* between nodes is exactly the same as the number of *potential connections.* You can’t draw any new lines to connect these nodes; they’re all already connected. They’re perfectly “dense.”

Now take a look at example C. Like example B, there are three nodes. But in this case, two of the nodes (the top and bottom ones) aren’t connected to each other. This little network is missing one of its *potential connections*, and as a result, its network density drops to two-out-of-three, or 66.7%.

To scale things up with a bit larger example, let’s say a grocery store has a customer network with a hundred people in it. The total number of *potential* connections between these customers is 4,950 (“n” multiplied by “n-1” divided by two). So, if, of those *potential* connections, there are only 495 *actual* connections, the network density would be 10%. If the number of actual connections were 2,475, then the network density would be 50%.

There you go. Now you know how to calculate network density. Here’s some more good stuff about networks.

Thank you for your explanation. It does really help me to understand the way to calculate the density. I just have a question about reading the number of density then. I actually used the KH coder to analyse textual data for word network and it says the value of density is .087. And I am not sure what it does mean. Does mean the nodes and edges have dense connections or not? What is the standard value of the density?

Moon

BSc undergraduate, University of Surrey

Not really, the maximum value is 1. So 0.087 would be 8.7%, not really dense but still somewhat connected 🙂