NessnetComplexity and social systems
Research theme

Social Networks: Structure, Spread and Influence

Almost every social process runs over a network. The structure of that network decides how fast things spread, who is exposed to what, and whether a local disturbance stays local.

Network science gave the social sciences a vocabulary that turned out to be unusually portable. The same measures describe collaboration between researchers, trade between countries, retweets between accounts and contact between people during an epidemic. That portability is a strength and a trap: the mathematics transfers cleanly, the interpretation does not.

Structure comes first

Before asking how something spreads, you need to know what it is spreading through. Degree distributions that are heavily skewed mean a handful of nodes touch most of the system. Short average path lengths mean nothing stays far away for long. Community structure means the network is really several loosely coupled networks. Each of these changes the answer to every downstream question. The structure and measures page works through the ones worth knowing.

Then spread

Diffusion on a network is not one process but at least two. Simple contagion needs a single exposure - this is how a news item or a virus moves. Complex contagion needs reinforcement from several independent contacts before someone adopts, which is how most behaviour and most opinion change works. The two produce opposite predictions about which network structures spread things fastest, which is why the distinction matters. See how opinions spread.

And then the media layer

Online platforms do not just carry a social network, they curate it. Ranking algorithms decide which of your contacts you actually see, which makes the effective network different from the nominal one. That is the subject of media, polarisation and misinformation, and it connects directly to work on digital traces, since the same logs that make platform research possible are the ones that make it ethically fraught.

In this theme

Notes under Social networks

Related

Other themes

  • Complexity economics Why treating an economy as a system in permanent motion rather than a machine that settles into equilibrium changes what you measure, model and forecast.
  • Agent-based modelling How agent-based models are built, what they are good for, where they fail, and which software people actually use.
  • Data and society What large behavioural datasets can and cannot tell you about a society, and what happens when policy is built on them.