Earlier, I wrote about network mapping at the Third Global Symposium on Health Systems Research. We mapped participants' social ties, collaboration ties, and information seeking ties as reported by them on an online survey.
Today I will show you the collaboration network generated through this process, as well as the 'real' network of co-authorship relationships generated through data mining.
We asked: "With whom have you collaborated on health systems or policy research in the past two years." Here's what we got:
In this graph, 91% of all nodes belong to a single, connected component. And, any two people in this network are connected by four or fewer people. We knew that the HSR community was… well-connected… (insular also comes to mind), but really?
In network science this is referred to as the 'small-world phenomenon.' Ever heard of 'six degrees of separation'? This refers to the fact that any two people in the United States are connected by six or fewer acquaintances (as confirmed by the famous postcard experiment of Stanley Milgram). Or that everyone in the movie world is connected to Kevin Bacon* by four or fewer connections (you might have also heard six).
In other words, Kevin Bacon is connected to the most other actors in the fewest steps. Why does this matter? Imagine that you had an important message to deliver to everyone in your network, and that the message travels from person to person starting with one person. Who would you give it to for fastest diffusion? Test your intuition here by trying to slow the spread of an infectious disease in a network.
Bottom line, you want to find the person who can access different parts of the network in the fewest steps. They sit on paths between the most number of people. They have the highest betweenness centrality.
But, our network data were not complete. Only about 70 people completed the survey, so we have plenty of missing edges and attributes for those who didn't complete the survey.
This begs two questions:
1. Can we mine citation data to get a fuller picture?
2. Who is the Kevin Bacon of HSR?
To answer the first, yes, we can use publicly available citation data. I searched ISI/Web of Science for all the names of Health Systems Global members (which was our proxy for who attended the conference), and downloaded the names of everyone they have published with since 2012, mirroring the survey question. Using Sci2, I constructed a co-authorship network with our respondents and their co-authors as nodes, and co-authorship relationships as edges/ties.
This is the network we get:
Complete co-authorship network of survey respondents
This network has 5576 nodes, representing 1433 publications since 2012. 75% of these authors are connected in one large component, with a relatively dense core but sparser periphery structure. We also see many more components -- a total of 191 unconnected smaller networks. No longer is everyone as connected. Now, like in the real world, everyone is connected by 6 degrees of separation, compared with 4 above.
Who has the shortest paths to everyone else in the network? Who is our Kevin Bacon?
Co-authorship network of survey respondents, nodes sized by (scaled) betweenness centrality,
six highest betweenness scored labeled
John Lavis has the highest betweenness centrality. John** was on the greatest number of paths between other actors. He is in a position to best connect otherwise unconnected parts of the network, and to reach all other nodes most efficiently, all else being equal.
When it comes to ideas, John Lavis is a broker (which is appropriate, as he studies knowledge brokers). He can:
- Access new ideas from diverse parts of the network
- Disseminate new ideas to diverse parts of the network
- Act as a translator between groups
What makes John different than the rest of us? Since 2012, he published 30 articles with 78 different people. That's an average of 2.6 new co-authors per paper! Imagine how many people they are connected to. And the fact that you might not recognize all the names is a good thing for innovation.
Does he intentionally try to broker the world of HSR? Maybe (we should ask him), but he also has many factors in his favor: he's an expert, so diverse authors seek him out and he's based in Canada where institutions are smaller and thus one cannot rely on within-institutional collaborations alone.
Back to the big picture. What does this mean for the field of Health Systems Research?
Connectivity is a good thing -- it's how we share and interpret ideas and knowledge, access resources and opportunities, and function as a society -- but there are two concerns with connectivity.
1. More connectivity isn't always better
2. The distribution of connections matter
First, there is a danger of networks becoming overly connected. Multiple, redundant connections with the same groups of people (represented by high network density or many triangles in a network) leads to 'group think' and may stymie innovation. Densely connected network cores begin to operate like an echo chamber.
Second, we need to think of how the connections are distributed across network nodes. At the node level, centrality (i.e., number of connections) equals social capital. We learned that betweenness centrality is one particularly powerful measure of capital, brokerage, and potential influence. The HSR co-authorship network had extremely large variation in people's betweenness scores, meaning that there is a lot of "power" in this network, and it is not evenly distributed.
So high network density is bad?
No. We need to separate connectedness into two components: density (i.e., the proportion of actor pairs who are connected) and centralization (i.e., the distribution of connections across the network). Density in and of itself isn't bad; sometimes all those repeated connections are necessary. It is the centralization of those connections around one or a few nodes that is not ideal for innovation. We used to think that density and decentralization were at odds but we now know they can co-exist.
Reza Yousefi-Nooraie found that academic medical research teams were more productive if their networks were dense, but also decentralized and open to external networks. A recent study in Nature, corroborating other studies of knowledge-intensive organizations, found that dense networks aid knowledge transfer when the knowledge is complex. The same study also found that team performance was positively associated with the strength of expressive ties (i.e., friendships).
However, I found in Burkina Faso that while dense networks aided the dissemination and spread of research evidence (i.e., complex information), the same density protected the policy status quo and prevented the new ideas from being adopted.
So what does success look like for a HSR network? An ideal academic co-authorship network would be productive (i.e., publish a large quantity of research) but it should also be innovative (i.e., willing to adopt new ideas) and relevant (i.e., research priorities are identified systematically and democratically and research findings are used). What would that look like as a network?
- Dense, with strong ties, including layered ties based on friendship and trust
- Decentralized, with brokers at the margins who can access external networks and new ideas
- Diverse, with varied representation of people who can translate across communities
How do we move towards such a network?
As individuals, we can continue to develop strong relationships with co-authors that lead to intellectual productivity. The HSR network, particularly the core, could be more decentralized. We could all be better at developing new collaborations with people outside of our existing networks. Finally, but related to the previous point, we can do better at building diverse research teams, as measured by geographic location, sex, age, discipline, and job role. At the network level, there are interventions that could be applied, but I can't think of examples where co-authorship networks are actively governed. What about research collaboration networks? Is this the role of Health Systems Global (or donors)?
* Note that newer data show that Sean Connery is on more shorter paths than Kevin Bacon.
** John was my PhD supervisor, but this in no way informed the network analysis.
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