Social Network Analysis of the Global Shapers Community

Posted on by Brandon Klein

THE GIST: As part of the World Economic Forum, the Global Shapers Community is a worldwide network of hubs that are developed and led by young people to work on projects that have the potential to make lasting positive contributions to society. Using the research tools from social network analysis I draw a few surprising inferences about the structure of this community.

I love studying networks. Looking into the deep structures of a complex social system can reveal important features and patterns of relations that can open our mind and broaden our horizons.

Networks provide extraordinary insights and new ways of thinking about the ordinary things that surround us. I am especially interested in social networks — the abstract patterns of relationships that we are continuously shaping in both our offline and online lives, as we interact with our families, co-workers, friends and strangers.

The interesting thing about visualising a network is that it allows you to take a bird’s eye perspective to discover the surprising connections around you. Social network analysis can be used not only to better understand how social groups are structured and how information spreads through these groups, but also to predict human behaviour.

As a member of the Global Shapers community, I had the privilege and unique opportunity to combine some of my research skills from my DPhil programme with the social impact activities at our local Oxford Hub. Over the past year, I have set up and managed our @OxfordShapers Twitter account and have tried to get a better understanding of the evolution of the Hub network from a broader academic perspective.

As of January 2014, there are now 4667 members of the Global Shapers community in 402 Hubs worldwide. But how is this community structured? What are the patterns of connections that govern the way how information flows through this social system? Given the global scale of the network, one would expect a fairly fragmented network with many internally-connected social clusters and only few connections between them.