Using Social Network Analysis to Depict the Structure of Research Collaborations

Sunday, 27 July 2014: 11:10 AM

Beth Baldwin Tigges, PhD, RN, CPNP, BC
College of Nursing, University of New Mexico, Albuquerque, NM
Shana Lane, MA
Clinical and Translational Science Center, University of New Mexico Health Sciences Center, Albuquerque, NM
Richard S. Larson, MD, PhD
Office of Research, University of New Mexico Health Sciences Center, Albuquerque, NM


The purpose of this study was to examine the structure of internal pilot grant collaborations in the first three years of the University of New Mexico Health Sciences Center (UNM HSC) Clinical and Translational Science Center (CTSC) in the U.S. using social network analysis.  Research funding agencies are increasingly prioritizing research that involves collaboration across multiple disciplines or specialties, institutions, or geographical locations.  Social network analysis is one analytic tool that is useful for depicting the structure of collaboration and changes over time.  In the U.S., initiatives such as the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program aim to transform academic health science centers and increase the speed with which basic scientific discoveries become widely disseminated health care interventions.  Two of the strategies used by many institutions with CTSAs to meet this aim are campus-wide pilot grant programs that provide preliminary data for extramural awards and structured opportunities for cross-disciplinary and -institutional collaboration.  Team science is viewed as one possible catalyst for rapid outcomes from translational research; team members can work on multiple different, yet complementary aspects of a scientific clinical problem simultaneously.  Pilot grant applications with multiple collaborators are often viewed more favorably by reviewers than single investigator proposals because of their potential for facilitating the formation of long-standing research teams.  Yet little is known about the characteristics of such teams, including their composition.  This study examined the structure of these collaborative research teams at one CTSA-funded institution in the U.S.


 Study Design:  The study was a secondary analysis based on retrospective document review of three years of awarded pilot grant applications at the UNM HSC NIH-funded CTSC.  The pilot grant program is open to principal investigators from any of the three colleges or schools at the UNM HSC (College of Nursing, College of Pharmacy, 19 departments from the School of Medicine).

Sample:  80 awarded pilot grant applications (Year 1 = 24, Year 2 = 34, Year 3 = 40)

Procedures:  Two separate reviewers evaluated the face pages and biosketches of the applications to identify the college or departmental affiliation of collaborators (inter-rater agreement = 90%).  The primary author determined final inclusion when there was disagreement.  Only internal UNM collaborations were included in this study.  Collaborators were defined as any faculty or post-doctoral fellow who was either listed as a co-investigator on the face page of the application or had an included biosketch.  Biostatisticians were not included if they did not have a faculty title or had a solely technical role in data analysis.  An instance of collaboration was defined as two collaborators from two different UNM colleges or departments, either inside or outside of the UNM HSC.  Collaborations within a single college or department were not counted.  Multiple collaborators from the same college or department on a single application, who were collaborating with someone from another college or department, were counted as one instance of collaboration only.

Measures:  Research collaborations were depicted visually using sociograms.  Each node in the sociogram represented a university college or department.  Edges in the sociogram (the lines between the nodes) represented collaborations on pilot grants.  Thicker edges depicted more collaboration.  Density was the number of total edges (collaborations) between colleges or departments divided by the maximum number of possible collaborations (normalized range 0-1, with 1 representing a “complete network”).  Degree centrality was the percent of all the direct collaborations that involved a given college or department.  Betweeness centrality measured the number of times a college or department needed a given college or department to reach another.  It measured position in the network.  College and departments were rank ordered in terms of their betweeness centrality.

Analysis:  Data matrices of collaboration counts were developed and entered into UCiNet software for analysis of network density, degree centrality, betweeness centrality, and generation of sociograms using compatible NetDraw software.


 Sociograms illustrated increased cumulative number and variety of research collaborations between colleges and departments over three years.  Stepped cumulative density increased from .10 (Year 1), to .22 (Years 1, 2), to .29 (Years 1, 2, 3) demonstrating new partnerships with each successive year and increasing network cohesion over time.  Collaborations were primarily within the UNM HSC, but there were collaborations within the broader UNM campus between a HSC college or department and UNM departments of Physics and Astronomy; Electrical and Computer Engineering; Psychology; or Health, Exercise, and Sports Science.  Departments that consistently had the highest degree centrality (for two or more of the three years) and had key roles in collaborations were all from the School of Medicine:  Internal Medicine (18% Year 1, 25% Year 2), Neurology (21% Year 1, 13% Year 3), and Pediatrics (15% Year 1, 13% Year 2). These collaborations tended to be with physicians of different specialties, rather than with PhD-prepared basic scientists, pharmacists, or nurses.  Radiology (12% Year 1), Biochemistry and Molecular Biology (10%), Molecular Genetics and Microbiology (17%), and Psychiatry (13%) also had high degree centrality in one year each.  The College of Pharmacy and the School of Medicine departments of Internal Medicine, Pediatrics, Molecular Genetics and Microbiology were the organizations with the most frequent betweeness centrality and the most strategic positions for facilitating collaboration.  The College of Nursing increased in density, degree centrality, and betweeness centrality between Years 1 and 3.  Nursing faculty collaborated with colleagues from Psychology, Internal Medicine, and Emergency Medicine.  Cumulative overall network centrality increased from 11% in Year 1 to 20% in Year 3.  Likewise, cumulative overall network betweeness increased from 13% in Year 1 to 35% in Year 3.


In the initial three years of a clinical and translational science pilot grant program at one CTSA-funded university in the U.S., new collaborations between investigators from different colleges and department continued to form and certain colleges and departments were consistently central to the formation of those partnerships.  Social network analysis is a useful tool for researchers from around the world for depicting the structure of research collaborations.  Feedback from the analyses may also be effective in encouraging investigators and organizations to either initiate collaboration for the first time or take a leadership role in facilitating collaboration within the institution.

This project was supported in full by the U.S. National Center for Research Resources and the U.S. National Center for Advancing Translational Sciences of the U.S. National Institutes of Health through Grant Number UL1 TR000041 (R. Larson, PI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH