The Center has an extensive history of applying SNA in our evaluation work. Here are just a few examples that can help you think about what SNA can do for you.

National Networks: Leadership Structure

When we evaluated CDC’s National Tobacco Control Networks for Special Populations, we found that their networks were responsive to the lead agencies’ design and director charisma.

“So the question is, if [the director] got hit by a bus today, would the organization be ok? And I don’t know… And when you have someone who is this amazing leader, who is personable and funny and excellent, essentially you have [to have] other people.”

 

This organization demonstrated a highly centralized network centered around a lead agency with a highly charismatic director. These kinds of structures can be very efficient, particularly with communication, but are also very vulnerable if the central partner gets knocked out. Indeed, there was some concern about sustainability, which was reflected in the quote. Seeing their concerns reflected in the data, the director did take a step back from some of the activities to allow others to come in and participate at a higher level than they did before.

“This network was created as a network of networks from the get-go. So it’s not like here it is, we are one organization and then we suddenly create a network. No. From the beginning, we decided to build it this way.”

This network was specifically designed to be less centralized, and the structure confirmed that their efforts were working.

Cross-Disciplinary Science: Change Over Time

Network maps are great for showing change over time. Here we see grant submissions for the Washington University Institute of Clinical and Translational Sciences. Each node is an investigator, investigators are color-coded by scientific discipline, and investigators are linked if they were Key Personnel together on a grant submission. One goal was to increase collaboration of research across different disciplines. The network on the left is from 2007 (pre-award) and the network on the right is after few years in. You can see that they have had some success. The 2010 network has greater density, people have more collaborators on average, and the decrease in modularity indicates an increase in the proportion of cross-disciplinary collaboration.

2007

2010

Year Size Density Ave. Degree Modularity
2007 186 0.9% 1.65 .140
2010 193 2.3% 4.41 .054

Emergency Planners in Missouri: Informing Decision Making

This is a communication network for public health emergency planners in Missouri. You would hope to see an emergency response network that is highly interconnected, which is the case for parts of the network. The left and right sides of the network are actually the east and west sides of the state. In the middle, G1 was the only major connector between planners in different parts of the state. It turns out that G1 was also nearing retirement. These findings were used to increase emergency planner communication and strengthen the network to ensure that the network wouldn’t be disrupted when G1 was no longer a part of it.

Policy Change: Communication Gaps

We evaluated grantee partnerships for the Communities Putting Prevention to Work (CPPW) network in St Louis in 2011, where the goal was to strengthen the St. Louis County tobacco ordinance. The network on the left displays importance nominations, and the network on the right displays who is in contact with each other. The red triangles represent the St. Louis County Council members.

Important

Contact

The Council members were recognized as being important to CPPW goals as shown in the importance network. However, when examining the Contact network, only one person was actually in contact with only one of the Council members. Comparing networks of different relationships in this way can highlight gaps where those who are important to the goals for a project are out of the loop in other activities.

Mentoring & Productivity: Modeling & Hypothesis Testing

We have evaluated the Implementation Research Institute (IRI) at Washington University in St. Louis for several consecutive years. The Institute brings fellows in for a week-long on-site training in mental health implementation science. Our evaluation examines several relationships between fellows and core faculty, including mentorship, publications, grant submissions, etc.

One finding was that the number of mentoring relationships a fellow had predicted the number of academic collaborative relationships they had two years later. We found that this relationship was stronger with a two-year lag between mentoring and collaboration than for a one-year lag, suggesting that the benefits of mentoring may require time to accrue.