Science and Leadership for the Future: Networks

Ross Dawson recently gave a keynote address on Science and Leadership for the Future to a small group of major media and corporate clients of New Scientist magazine.

Given the context, he was able to delve a little deeper into the issues than he would for most audiences. The video of his presentation was sliced into a number of brief segments. Below is the video of the section of his presentation on Networks. Please click here to view the complete presentation.

Full Transcription: Science and Leadership for the Future – Networks

Ross Dawson:

First let us look at networks, and we know we’ve heard the word networks in so many fronts…social networks & we understand that we live in a network world. What we’re starting to see is that the networks are fundamental, not just to our communication but indeed to many different aspects of our lives. There are a number of different types of topologies of networks, and one which has grown particular attention over the last years is that of Scale-free networks. Now, Scale-free network is one that has the same structure whatever the size, whether it’s a small size or a large size, and perhaps not surprisingly, the internet was the first network that was studied, which resulted in this perspective of scale-free networks. I remember back in 1993, first time I was on the internet, and those were the days when NCIS in the United States, every day, would have a list of all the new websites on the internet, and I would go on every day and say, look there’s another 10 or 20 websites. Obviously, the pace has increased to be rather faster today, yet the actual structure of the internet is still the same as what it was.

Now, this is achieved through what is called preferential attachment, which essentially means, as you get new nodes, those ones that already have many attachments are more likely to get additional attachments to them. So you start to get into this whole scaling of network & you get this structure. It’s important to distinguish between distributions where there’s a classic Gaussian distribution, a bell curve. An example of that is the height of people there tends to be an average and there are points where you go very short or very tall, you can predict how unusual that will be. And if you look for example, at a financial market, you tend to say, well there’s a certain domain of variability, and you can predict and build some kind of a bell curve around that distribution. Yet, in fact, it often does not meet that, there’s what’s called a fat tail on that bell curve. That’s called, one of my favorite words, the Heteroskedasticity, which points to this fat tail & means the things which you don’t think are going to be very likely, actually are a lot more likely than they’re likely to be.

In fact, what we are seeing is a shift towards the world where those extreme events are actually becoming more likely. If we contrast this Gaussian distribution with this power law distribution..you’ll be familiar with this idea of the long tail, which is being well popularized in media, where a scale-free network actually can be defined as having a power law distribution, which means that, essentially it has an exponential factor in it…you get a few which have very many connections and many which have a small number of connections. This is the case for the internet, it’s a case for media. In this there are a number of factors which are shifting many domains towards more of this power law distribution. In fact, when we start to see factors such as positive feedback loops, self-organization…these are factors in systems which actually shift them towards a power law or a distributional power network. This for example, is what is happening in organizations today.

We’ve classically had a structure, hierarchical structures which means there is a very narrow range of distribution in the number of communications that people have in organizations. When we move to the communication as possible within organizations, we actually shift towards more of a power law distribution. In fact, we are seeing these scale-free networks or close to scale-free networks across many domains, and some of these are human domains such as, how actors are connected in movies, in politics and social networks, in industries and organizations. These scale-free networks are actually applied in many other things that are fundamental to who we are. So proteins within cells, for example, have a scale-free network distribution, the neural connection within brains have a close to scale-free network distribution and we’re starting to see that there’re a number of other aspects of biological systems & transport systems, as well as the human system, which have a scale-free system.

So this means that many of the insights we can get from scale-free networks can be applied across many domains. If we look at just one domain which is the flows of information…I think many of you are very interested in how we can get information to flow more effectively. So, it’s interesting to track back and look at how that has developed over the last decade or so.

So early on when we started to define scale-free networks…the whole idea of influences and these hubs, and this idea of cascades…this idea of where you can get a signal, a bit of information which goes in turn to many others and cascades out from a single point. Early on when we’re looking at multi-agent models of networks and how information disseminates…started to get some insight which is still relevant today, that this depends on having a certain density of the networks, the homogeneity of the networks, how closely people are linked, and how likely they are to pass on information. And also this idea of a cascade window where if you look at how likely somebody is to pass it on, in fact, at a certain point, it starts to dampen.

So you actually get a window within which cascades are more likely to happen. Now, over the last years we are able to do some more empirical studies of how we get dissemination of information within flows. There’s actually a more recent study which shows a number of sets where there was extensive studies of dissemination of information through influence and networks, and finding that, in fact, there are very few cases where you actually truly get this cascade. These are a few examples of the configurations where you did get these cascades, where the blue dot in the middle is of a single influencer, and in a few cases they almost impacted, it was really just that first degree of their network which influenced where they actually had the impact.

It’s only a couple of cases where it went through a number of different degrees to be able to reach that, which points to the fact that this, the influencers and how homogeneous their networks are, is in fact, a key determinant of how information flows out through these networks, but what we’re seeing, this is just one example of being able to understand the topology of networks and be able to facilitate how we can spread information more effectively.

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