SSNAP: Scientific Social Network Analysis Project (2016-2017)

Modern scientific production is a collective affair. Researchers build on each other’s work and collaborate to form latent communities around methods, topics and ideas. This collective activity forms a network of scientists that captures the social substrate of scientific productivity. The collective nature of scientific production has grown over time, with larger scientific teams, greater interdisciplinary production and rapid expansion of electronic collaboration as well as the development of collective knowledge products such as Wikipedia.

This Bass Connections project set out to use the tools of modern network analysis to understand multiple aspects of science production, content and growth. To accomplish this goal, the team built representative corpuses for multiple broad topical areas (“fields”) and constructed the dynamic collaboration and bibliographic networks within each field.

Team members began by reviewing basic theory and computation for network models, including statistical and sociological perspectives that shape this effort, as well as learning how to use various programming tools. Next the team collected data from all papers indexed in the Web of Science in six disciplines (primatology, virology, cognitive neuroscience, political science, sociology, philosophy) and two interdisciplinary research areas (social network research, artificial intelligence). Team members built analysis files using text parsing tools to generate topic networks (based on common bibliographic information and/or content of the abstract/titles of each paper) with disambiguated names matched to gender (from social security and international data sources). After cleaning the data, the team constructed clusters, mapped the networks or otherwise deployed these data to answer specific research questions.

Going forward, this work will focus on modeling the returns to collaboration by gender, topical segregation of scientists, rhetorical change in artificial intelligence and linkages between investigator identity and topics.

As part of this project, team members took a close-up look at Duke’s scholarly networks to examine collaborations within different fields of study within the larger research community. Focusing on where scholars fit into Duke’s “intellectual space” rather than its physical space, the team used language processing software to analyze the titles and abstracts of publications listed in the Scholars@Duke database and mapped the papers according to similar use of terms.

Timing

Summer 2016 – Spring 2017

Team Outcomes

SSNAP: Scientific Social Network Analysis Project (James Moody, Greg Appelbaum, Laura Sheble, Taylor Brown, Evan Donahue, Jonathan Morgan, Crystal Peoples, Rafael Ventura, Magdalena Daveka, Anne Driscoll, Arthur Kwan, Madhavi Rajiv, Devesh Sharma, Kanan Shaw, Maria Sison)

Toward an Intellectual Atlas of Scholars@Duke

This Team in the News

Webs of Minds and Ideas Bind Duke’s Campus

Faculty/Staff Team Members

Lawrence Appelbaum, School of Medicine-Psychiatry*
Christopher Bail, Arts & Sciences-Sociology
Scott Huettel, Trinity - Psychology and Neuroscience
James Moody, Trinity - Sociology*
Seth Sanders, Trinity - Economics
Laura Sheble, Duke Network Analysis Center (DNAC)

Graduate Team Members

Taylor Brown, Sociology-PHD
Evan Donahue, Cmp Media, Arts & Cultures-PhD
Jonathan Morgan, Sociology-PHD
Crystal Peoples, Sociology-PHD
Rafael Ventura, Philosophy-PHD

Undergraduate Team Members

Magdalena Dakeva, Linguistics (AB), Computer Science (AB2)
Anne Driscoll, Economics (BS)
Mike Gao, Mathematics (BS), Economics (BS2)
Benjamin Clay McMullen
Muhammad Mubin, Computer Science (BS), Economics (BS2)
Madhavi Rajiv, Electrical & Computer Egr(BSE), Philosophy (AB2)
Devesh Sharma, Computer Science (BS)
Thamina Stoll, Political Science (AB)
Arthur Kwan Hung Wu, Statistical Science (BS), Computer Science (BS2)
Steven Yang, Computer Science (AB), Statistical Science (AB2)

* denotes team leader

Status

Completed, Archived