notes: terje rasmussen on sociological approaches to networks
This is a lovely compact intro, which is perfect for a non-sociologist like me 🙂 Quick notes follow.
Superimportant articles that everyone quotes (especially the first)
Mark Granovetter: “The Strength of Weak Ties”, American Journal of Sociology, vol 78, 1973.
Mark Granovetter: “The Strength of Weak Ties: A Network Theory Revisited.” Sociological Theory, Vol 1 1983.
[Goodness, Terje’s a very clear, calm lecturer. I bet his students are happy.]
Granovetter studied people in Chicago, asked how they got a job. Slightly more than half got their jobs through aquaintances – not advertisements or good friends. (I’ve read about this before, I realise.)
Granovetter lifts this up from the micro-level and his own empirical studies to examine how society works. Not a total theory like Durkheim, but a starting point (?).
It’s really unlikely that A knows C and A knows B but B and C don’t know each other, at any rate if A is pretty good friends with both B and C: B and C will probably know each other too.
If A needs a job, she’ll ask B and C. They probably won’t have any new information, because A already shares most of the information that B and C have. There’s a far greater chance A will get new information — for instance about a job that might suit A — from her weak ties, that is from aquaintances and people that she doesn’t see very often. The greater social distance between A and D means that D knows more things that A doesn’t already know.
Weak ties also important because they work as bridges between social groups. People who are bridges between two groups may appear to be socially isolated but actually have weak ties with two or more groups which gives them very early access to new information.
Small worlds networks – Stanley Milgram’s experiments in the 60s (he was the guy who made people think they tortured other people which led to severe trauma and raised questions of research ethics) – another of his experiments was asking people to send a parcel to a person in another city via friends and aquaintances. Using some creative arithmetics, Milgram worked out that every person on the planet is connected by a maximum of six degrees (A knows B knows C knows D etc) It’s the bridges (people connecting groups) that make the world seem this small.
[I’ve heard or read all this somewhere but for some reason I’m enjoying listening and typing it out. Maybe because I hadn’t realised that I already knew social networks 101 and it’s satisfying to discover it.]
This discovery casts new light on other sociological issues.
For instance, there was a very tightly integrated group of people in a suburb of Boston whose suburb was going to be destroyed by a freeway. Sociologists thought they’d fight the renovations, because of the strong social network. But no, they did nothing! Another suburb in Boston, less tightly integrated, fought efficiently against a similar situation.
Granovetter’s analysis: the first suburb had lots of families with few connections out of their group, whereas the others worked various places and had lots of weak ties to other areas of society and therefore successfully organised. Ethnical minorities easily end up without enough weak ties out of their own group.
If you want to learn how to write a good article in social science, the first of Granovetter’s articles about is a perfect example, Terje says. He has a clear, simple idea, and uses it to show problems (?) in previous research and shows how the idea suggests new ways of viewing the world (or something).
Sociologists like using averages to explain things, though of course they know that averages hide differences that are crucial. Quantitative network theory has always assumed that networks are randomised. We all have the same number of social relations in their graphs, although of course that’s not true.
[Ooh, we’re coming to the power law, aren’t we! Cool, reading Many2many for a year or two is equivalent to Sociology 101! Or at least to a half hour lecture on sociological network theory.]
Graph of power law graph with some getting lots and most getting not that much. You both get individuals with a lot more or fewer relationships, and clusters that are more densely interlinked (hubs).
Sociologists have been too keen on talking about distributed networks — e.g. Paul Baran’s drawing of centralised, decentralised and distributed networks, where he described how the Internet should be set up. In a distributed network everything’s equally distributed, everyone’s equal, you know. If we imagine that network to be a social network, it’d be a network that was less efficient than a decentralised network which has hubs, clusters and strong and weak ties.
Will digital technology support strong and weak ties? How can we uses these ways of thinking about social networks in thinking about technology and social software? Should we aim at adecentralised rather than a distributed structure?