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Social networks: What makes you you, and will you get the flu?

Stephen Davis


Dr Stephen Davis explains how complex networks shape everything from our political opinions to how likely you are to catch or spread the flu.

Are you really you? I mean, is it really you who determines your political opinions, your religious beliefs and your perception of what a normal healthy weight is, or is it your social context whereby your perception of what is normal and right is dictated by your friends and family?

The answer is that your social network probably plays a much larger role than you realise.

For example, studies have shown that diseases such as obesity spread through human social networks much like a virus does.

How complex networks affect our lives is an emerging area of mathematical science. A network consists of nodes, which represent discrete things such as people or genes or websites, and edges that indicate connections between nodes.

A network is complex if it has many nodes and its edges are neither random nor predictable. Human society is a real complex network, as is the collection of neurons in our brains and the protein interactions that govern our cell function.

Research into the networks arising at all of these scales has led to the stunning discovery that they all have a common topology:

  1. 1.They consist of many nodes with few connections and some nodes that are mega-enormous connected hubs
  2. 2.If A connects to B and A connects to C then it is highly likely that B connects to C
  3. 3.They are much easier to 'get around' than you'd think, which is the concept popularly known in human society as six degrees of separation.

The consequences for health, society and disease are profound.

The presence of hubs in sexual networks, for example, led early theoreticians to the hasty conclusion that sexually-transmitted diseases such as HIV and AIDS cannot be controlled.

In fact, distributions of connectedness mean that public health authorities can target people who are the most responsible for transmitting disease. Even if these individuals are unknown they can be reached by choosing people at random, and then vaccinating one or more of their contacts. This strategy leads to the treatment of highly connected individuals just because they are highly connected.

The Kevin Bacon factor

One of the key questions that arises in complex networks is how to identify which nodes are central or important in a network.

In public health, the number of edges a node has is important, but this in other contexts is akin to ranking academics by how many publications they have or actors by how many films they appear in. This would then seem overly simplistic because, for example, it doesn't take into account the quality of the film or the paper, or who the co-actors or co-authors are.

Bacon numbers arose when actor Kevin Bacon commented that he had worked with everybody in Hollywood or someone who's worked with them. The closest node in a network is the one for which the number of hops to it, starting from everywhere else in the network, is smallest. It turned out that the centrality that Kevin Bacon claimed in the movie-actor network was not terribly true — he isn't in the top 100 actors ranked in terms of closeness.

The exercise in the box above will give you an idea of how connected you are.

Connectedness and disease do go hand in hand. If you are a highly social person (a hub in your social network) you are more likely to get sick and you'll be one of the first to get sick. In that case make sure you get the flu vaccine.

Networks grow over time

The researchers that first discovered that real networks have a common topology also realised that real networks are not static but grow over time.

They proposed a universal mechanism called preferential attachment: that when a new node is added then it preferentially links to nodes in the network that are already well connected. This is known as preferential attachment and it is enough to explain the presence of giant hubs as well as the high frequency of nodes with few connections.

The most exciting thing about the study of real complex networks is that they show us the fundamental principles that govern self-organising structure, of which our broader society, our languages, as well as our cell and brain function, are examples of.

Indeed, it has been discovered that all real networks have communities, and then communities within communities, and all networks have repeatedly recurring building blocks that make up the larger network.

It will be fascinating to see what next complex networks reveal about the world around us, about society and about ourselves.

Me or my network?

What determines our fate? What determines how successful we are in our careers and relationships? Is it our individual attributes, or is it our position in our social and professional networks? Would we perform better if we were simply placed differently, in a more central position in our networks?

The answer to the last question is absolutely yes. Even when we consider how long we will live, social network studies have shown that a large network later in life increases our survival by 22 per cent.

With such profound implications, to be more conscious of our network and our friendships would seem to be wise. Of course, such wisdom has existed in anecdotal form for centuries ("choose your friends wisely"), but network science is making it harder and harder to ignore.


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