Ep. 2 - The Underlying Properties Of Network Theory (NFX Masterclass)

SPEAKER_1 (0:00)

You're listening to episode two, The Underlying Properties of Network Theory from the NetworkFX Masterclass, here on the NFX Podcast. To watch the course, visit nfx.com/masterclass.

James Currier (0:14)

Okay, let's start at the beginning. What are network effects? So basic idea of network effects is that every new user of your product adds value to the other users of your product. That's a fundamental definition. There's lots of different network effects which fall into that category, but that's the basic idea. Now, most people kind of stop there and say there are network effects or not. Just not the case. Most people don't understand if you look closely at these things, you notice they behave differently. Okay, they act differently. They have different mechanics to them. How you would build your product to enhance them or to grow them is different, depending on which network effects you're actually building.

Fundamental properties of a network are essentially, you've got links and nodes. So a node could be a person and a link links them together. And now you could have a telephone be a node, you could have a person be a node, you could have a three-sided marketplace where you've got three different types of nodes. You have 20 different types of nodes, and you got different structures. You might have a network structure where everyone is an equal, and they're all connected equally. You might have a hierarchical network structure where people are unequal, but they're still all connected in what is a network. Okay, you could have a two-sided marketplace network structure where people are different, but they're all connected through your marketplace. So you have lots of different types of structures, but they're all constructed of nodes and links. Now on those links could travel money, can travel status, can travel information. There's a whole bunch of things that move across these links, and different networks have different things moving across those links between the nodes. Okay, and then the network has size, how many nodes are in a network, and then it has density, how many links are between the different nodes. So for instance, a Spotify with connections between people and artists is probably pretty low compared to something like a LinkedIn, where the number of connections between people is very high. People have 500, 2000, or 5000 connections between people. So your density ends up being quite high. Those are two core differences between different types of networks. For instance, a Twitter network, you might have 3% of people producing 90% of the tweets and having most people connect into them, and then the other folks, they have a very low density connection. Now once you get to high density, you get what we call the white hot center. So if there's one node where everybody's connecting to them, then you've got the beginnings of a white hot center with the core node and then the nodes that are closest to them. Another important aspect of these links is that they can be directed. There can be direction to these. So for instance, in Twitter, you would have an influencer who is important sending out mostly messages to people who are just reading. That would be directional in one way. Or you could have, let's say, a Venmo where people are paying each other. That would be bi-directional.

Network structures actually have different shapes to them. The simplest one, of course, is one-to-one, where you could have a network where it's just one person or another person, where two nodes are connected by one link. Then you get into network structures which look more like one-to-many. Think of a TV broadcast station in the 1900s, or a radio station, or a newspaper. And then you get into the many-to-many, which was really the innovation of the Internet that came along in the 1990s, where suddenly all the nodes could see each other, and it didn't have to be coming from one central source. What's also fascinating is that we then have laws that are associated with these different network structures. For instance, in the 1960s, you had a guy named Sarnoff, who was sort of a broadcast titan. And so he understood this network structure, and he saw that the value of the network increased with the number of nodes in the network on a one-to-one basis. Metcalf comes along in the 80s, and he was the father of Ethernet, where he was connecting all of the computers together with the first Ethernet cables and whatnot. And he noticed that the value was not increasing linearly with the number of nodes, but it was increasing geometrically, so a sort of geometric growth rate to the value. And then around 1999, along comes a professor from MIT and says, well, Metcalfe is correct, but it's actually more intense than that, because if you look within networks, you have clusters of people. Let's say a parenting group or a book club or a soccer team. They are networking on your network, and their connection to themselves creates its own very highly dense network effect. And because they're dedicated to this network, the network's value actually increases at a much more geometric rate, 2 to the value of n. And so these things are directionally correct, even if they're not precise at any one time. But you can actually calculate and map the growth of the value of Facebook to Metcalfe's Law in particular. And it's actually not too far off. So these are useful ways of understanding how much value you're creating by how big you build the network, and how much value is at the end of your process and why you should be paying attention to this. Now, this also shows you that over the last few decades, we've been looking for ways of understanding and explaining networks because we're just at the point of being able to see them. They've always been there for thousands of years, but we now are able to see them and measure them. And you, just by watching this masterclass, are actually participating in the continued discovery and understanding of these networks and how they function. And so the prize for you to understand this and use them appropriately is very, very high because of this geometric growth pattern of the value of these networks. How do you build that? Well, you've got to get to a critical mass. Okay, you've got to actually get your network to tip. Now, one of the big ways of doing that is by looking at your clustering coefficient, because not all nodes are created equal. You're going to want to focus on very particular types of nodes and get them to cluster, get them to create their own network effects, and then bridge them to other networks in many cases. That will help you get to a tipping point where the network is growing and growing, but then at some point, the value starts to take off almost by itself. But getting from here to here is your job as a founder, and it's not easy. Okay, so figuring out your path to that, which we'll talk about later with network bonding theory, figuring that out and building these clustering coefficients to get the network to take off is one of your jobs, and that's what we want to talk about. So to build the network value, you need to get to critical mass. And what happens is, as your network is growing a little bit, a little bit, a little bit all the time, at some point, it hits a moment of critical mass where it takes off, it lifts off, you can feel it. It's like someone stuck two fingers in your nose and just yanks your head forward. It just starts to take off on its own. And that critical mass moment is often facilitated through developing your clustering. We just mentioned how Reed's Law really focused in on what the clusters are inside of your overall network. Not all nodes are created equal. In fact, they're really created differently. You need to focus on the different types and cluster them together, and then bridge the different clusters to each other on your way toward reaching that critical mass. So being able to see and measure and work with your clustering coefficients on the different groups is actually an important skill. Now to get these clusters going, you're going to want to find your minimal viable cluster. Some small group of people that once attach to each other will stay attached to each other that you can then build on. Again, maybe creating another cluster and then bridging those two clusters. That minimal viable cluster really helps you to found the overall network. Now, to get to critical mass, the product is going to have to have some value to these users, to this cluster. But at some point, you're going to notice that the value of the network exceeds the value of the product. Your software, your system is going to provide some value, but then eventually the network takes over in terms of where the value is coming from. And that's when you've hit critical mass. That's when those two fingers get you in the nose and pull your head forward. You'll feel it.