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Social Network Structure

Network structure refers to factors that shape the overall topology to a social network

Network structure refers to factors that shape the overall topology to a social network

Social network structure describes the makeup of the overall social network. There are a number of major factors shaping the overall make-up to a social network including the density of connections being a primary factor as it tells us how connected an agent is on average;  the average path length is a second key overall metric as it tells us how close together any two agents are on average. Likewise, degree distribution also plays an important role in defining the degree of equality within the system.1

Network Density

Overall network density is a primary determinant in the make-up of any network, with density we are simply asking how many connections are there relative to the maximum possible number of connections. Network density can be understood in terms of interaction cost, the easier it is for agents to make connections the more connections we are likely to have. Network density is a very fundamental parameter in that it defines the difference between a social system that is a network as opposed to being simply a group of people. At a low level of connectivity we are dealing mainly with individuals in isolation, here it is the attributes and properties of those individuals in isolation that matters. When we turn up the connectivity this is no longer so much the case, it is now the nature of the network that you are a part of that matters, this is captured in the famous saying “it is not what you know but who you know”.
If we are dealing with a social system with a low level of overall connectivity then it does matter what you as an individual know, but at a higher density of connections, it is more what your network knows that matters. We see this with the internet, we ourselves used to have most of the information and knowledge that we would use on a daily basis but now much more of the information we use, we do not have ourselves but is instead in the network. Thus as we turn up the degree of connectivity within the social system it is no longer the attributes of the agents in isolation that is so important but instead their capacity to interoperate, provide something of value to the network and ensure their connectivity to that network. As another example we might think about the difference between so-called introverted and extroverted people, introverted people with a low level of social connectivity have to rely heavily on their own capabilities and they are often more self-resourceful, whereas extroverts who can rely heavily on their network may not have such personal capabilities but are instead particularly good at accessing the skills and resources they lack through their social network. Thus the overall connectivity is a primary determinant within any social system and also one of the determinants to the nature of power within the organization as, within a social system that has a very low density and loose coupling, not much power can be exerted; in high-density social systems there are more and stronger channels through which power can be exerted.

Average Path Length

Average path length is important in determining how "close" people perceive they are to others within the social system and may play an important role in determining social cohesion

Average path length is important in determining how “close” people perceive they are to others within the social system and may play an important role in determining social cohesion

Network density is also a key determinant to average path length, here we are talking about how close any two agents within the network are, to each other on average, this closeness is obviously a very important factor in terms of cohesion and interdependence. As we scale up the number of components to the social system this creates longer path lengths between members, this can stretch and break traditional forms of social cohesion, a longer average path length is like an outward force disintegrating the social system as it puts people at a longer distance from each other with a lower sense of interdependency. We can also note that the longer the path length the easier it is for sub groups to form and disintegrate the overall social network. Agents act and adapt to their local environment, if we turn up the average path length between agents or groups they will not identify with, or adapt to those other members and we may get the formation of incompatible local clusters, trying to achieve global coordination within such a system we would likely mean having to impose it in some top down fashion. But now if we turn down the average path length, which could happen through better transportation or communications technology, people now interact more often making it easier for them to synchronize their states and easier for them to recognize their interdependence and common identity.2

Degree Distribution

Another important question we may ask about the over structure to a social network relates to its degree distribution. Degree distribution is a measurement of how evenly or unevenly the degree of connectivity is distributed out among the agents, degree distribution is important because it is really telling us how equal or unequal resources are distributed out in the system, it is asking the question do some people have a lot of connections and others have very few? Or does everyone have roughly the same degree? This is going to tell us a lot about the nature of power within that system. High degree distribution will mean inequality of some kind that will be detrimental to social integration and it is in many ways this inequality in connectivity that is the means through which power can be exerted.
This degree distribution tells us a lot about how centralized or distributed the network structure is. At a low degree distribution, all actors have relatively the same amount of connections, thus they would be what we consider peers and we would get many peer-to-peer interactions giving us a distributed network. As an example of a distributed social system we might think of the Israeli Kibbutzim which are collective communities in Israel that were traditionally based on agriculture. Within the Kibbutzim, the principle of equality was taken very seriously up until recently, members did not individually own tools or even clothing, they ate meals together in the communal dining hall and major decisions about the future of the community were made by consensus or by a vote amongst all. Distributed social systems like this have limited centralized institutions everyone is responsible for maintaining the system and power is thus distributed out. Although distributed social networks may exist they are often the product of some random process, or a small informal network, or a network in its early formation where it has not developed any overall formal organization or as in this example of the Kibbutz the social network has been specifically designed to be egalitarian in nature.

Scale-Free Networks

But more often what we see is that as a social network develops and particularly when it becomes more formal, we get greater differentiation between degrees of connectivity, many real-world social networks show a skewed node-degree distribution in which most nodes have only a few links but, by contrast, there exist some nodes which are extremely well connected. This heavy-tailed distribution is known as a power-law or scale-free network. Here we are getting the emergence of major hubs and high degrees of social inequality.
There may be two different reasons for this inequality, firstly some people are simply better at doing certain things than others, we all watch certain people play football, sing or act simply because they are better at it than others, and what we mean by that is that they provide us with a better return on our investment of time, energy or money and thus many of us choose to make connections to that particular node while others do not receive our attention, thus giving us this unequal, centralized model and this process is meritocratic in nature. This explanation is largely intuitive to us but it might not be sufficed to explain how we can get such extreme differences in connectivity within these scale free social networks. Researchers have then also come up with another explanation for the formation of these scale-free networks, that of preferential attachment.3

Preferential Attachment

Preferential attachment plays an important role in the formation of social networks and can be identified as a primary driver behind social inequality

Preferential attachment plays an important role in the formation of social networks and can be identified as a primary driver behind social inequality

A preferential attachment process is any of a class of processes in which some quantity, typically some form of wealth or attention, is distributed among a number of individuals or objects according to how much they already have so that those who already have lots receive more and those who already have little received less. The best example of how preferential attachment works is seen in recent research done by Duncan Watts and team, where they created two websites selling music tracks, one where people could rate the songs they downloaded and one where they could not. over 14,000 participants then downloaded previously unknown songs on both sides. On the site where users could leave feedback for each track and others could see that feedback, it was found that there was a much greater disparity between the most downloaded song and the least compared to the other site where there was no feedback available, thus increasing the strength of social influence increased the inequality in degree distribution.
This power law distribution also applies to cities, the distribution of wealth and income and many other phenomena where we have social interaction creating feedback loops that amplify the disparity to give us a much greater degree distribution than would occur if simply generated by merit, and this creates major centralized hubs within the network, weather we are talking about an urban network, financial network or some other social network.4

Cite this article as: Joss Colchester, "Social Network Structure," in Complexity Labs, January 7, 2016, http://complexitylabs.io/social-network-structure/.
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2017-05-25T19:42:25+00:00