This week I’m attending my first conference as a PhD candidate. It’s called Sunbelt 2013, and because it is in Hamburg and it rains non-stop, it better could be named Rainbelt. Nonetheless, this is the place to be this week for network researchers. And as I’m studying social networks, I’m glad to be here as well.
As a newbie, I can see it’s definitely a place where people meet old friends, make new friends and where it’s fun being part of it. Especially when on the Thursday, Friday and Saturday nights the drinks are included.
Yesterday and today I attended a workshop on dynamic networks and the software program ORA presented by Jürgen Pfeffer from the Carnegie Mellon university. A great tool, and when you are looking at temporal data, I think it’s essential. Probably a tool I can use in the near future.
Starting this afternoon, and continuing until Sunday morning, there are sessions where everyone can present their work. There are so many parallel sessions, that you will miss most of them because you have to choose carefully which ones to attend.
And as far as going into town and see what Hamburg has to offer, there is limited time for that as the Sunbelt program is very full, and I have to prepare for my presentation on Saturday morning as well. I will talk about people who bridge online groups by being an active member in multiple groups (which is slightly different from what you read behind the link). The data for the research comes from the online community we support at one of our clients from Favela Fabric, where I work.
At the picture below you see my view at the moment, it looks kind of silent and peaceful, but it’s getting busier every minute. Now I will continue to prepare my presentation while I still have some time…
I’ve been reading literature about social network analysis (SNA) lately for my research. A lot is written about SNA. About analyzing, about measures, about SNA in organizations, and many more. However, many research does not address the value of the potential (or past) changes in the network, it especially addresses the value (social capital) of a snapshot of the network, the value of the existing social ties. Think about measures like density, distance, centrality, bridges, structural holes and weak ties, or, more qualitatively, trust, norms, power and autonomy.
In my view, it is not complete to study social networks as static. They were formed sometime before the analysis, there are reasons it became that way. It’s a bit like the universe, it changes continuously with changing nodes, relations and meaning of the relationships between nodes. Analysis of a network is always a snapshot in time. How the network will or can evolve is at least as interesting and important, because that will determine a snapshot at a later moment. The (social) reality that we live in now, is determined by the earlier realities, and the current reality will influence the possible future realities. Therefore we cannot deny the dynamic nature of a social network.
Now, can we determine the possible future directions of a network, for example in organizations? Can we identify what determined the current state of a social network? With the advance of online communities, and the vast amount of recorded data of relations and communication between people, perhaps we can. What network characteristics in the past influences the current network as it is? We can look at new entrants (nodes) which brings new opportunities, new knowledge, new relations over time. We can also look at nodes that disappeared (left the company) or changed position (got promoted). We can look at changing goals of individuals, departments or the company, and we can look at changing outside conditions (legislation, competitors, drastic events). There are many more things we can look at.
Not everything that we can identify in a social network snapshot is because of chance or fate. We probably can point to events in history that influenced the current state of the network. People made changes in their network themselves, or outside events triggered changes. Events can also be gradual, like the growing of a particular group within a network, that caused some change elsewhere in the network, which is an important asset in the current state.
So I’m thinking about looking for social capital measures in dynamic social networks, in the context of organizations, by comparing multiple snapshots in the past. It can hopefully be used to explain how networks work, and how they can evolve by making possible scenario’s, and what is needed to go for a preferred scenario. Do you think this would be interesting?