“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 bridges between nodes are technically called “edges,” but for the purpose of this article I simply refer to them with the less technical term, “connections.”
“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.
Glad to see Vital Edge again! This was interesting and I undertood it!
Thanks, Bill! I’m glad.
Great to read your writing again, Prof Rosenblatt is back!
Thanks, Doug. The writing may well fire back up again, but this notification was an accident — an older article.
Gideon: glad you’re back in my email. This was vey clear. Are you going to write a follow-up piece on the applications of this concept?
Thanks, Jill. Actually, one of the plug-ins for my WordPress site destroyed some of my older posts and so I had to re-create them and accidentally fired off notifications on a couple. Sorry about that. Must admit that the writing urge is creeping back though.