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<p>We used Social Network Analysis (SNA) and self-reported measures of transdisciplinary orientation to uncover the types of collaborative communities, broker, and integrator roles that emerged in a large convergence research team and examined how these communities and roles correspond to team members’ network positions and orientations. We modeled an undirected, weighted collaboration network using twenty team members’ levels and frequencies of collaboration with peers and contextualized the network patterns with open-ended responses on team dynamics. We overlaid community detection and node-level metrics (degree, betweenness centrality, clustering coefficient) with team members’ disciplinary backgrounds, cross-disciplinary ties, and transdisciplinary orientation. We identified three collaborative communities: a leadership core of <i>experienced integrators</i>, <i>mentor-mentee pairs,</i> and <i>domain anchors</i> who provide technical expertise. Broker (<i>major brokers, information carriers, satellite collaborators</i>) and integrator (<i>cross-cluster, hidden, within-cluster, narrow</i>) role classification revealed that boundary spanning depends on the interplay of personal orientation, opportunity, and project context. Influence was distributed beyond formal leadership, boundary spanning was not determined by seniority, and subgroup expertise and between-group reach reinforced each other. We outline practical applications of SNA for the evaluation and design of scientific teams that aim for knowledge integration across disciplinary boundaries.</p>

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Collaborative Communities, Brokers, and Integrators in a Convergence Research Team: A Social Network Analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月21日

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  • Shruti Punjabi,
  • Shalini Misra,
  • Megan A. Rippy,
  • Stanley B. Grant

摘要

We used Social Network Analysis (SNA) and self-reported measures of transdisciplinary orientation to uncover the types of collaborative communities, broker, and integrator roles that emerged in a large convergence research team and examined how these communities and roles correspond to team members’ network positions and orientations. We modeled an undirected, weighted collaboration network using twenty team members’ levels and frequencies of collaboration with peers and contextualized the network patterns with open-ended responses on team dynamics. We overlaid community detection and node-level metrics (degree, betweenness centrality, clustering coefficient) with team members’ disciplinary backgrounds, cross-disciplinary ties, and transdisciplinary orientation. We identified three collaborative communities: a leadership core of experienced integrators, mentor-mentee pairs, and domain anchors who provide technical expertise. Broker (major brokers, information carriers, satellite collaborators) and integrator (cross-cluster, hidden, within-cluster, narrow) role classification revealed that boundary spanning depends on the interplay of personal orientation, opportunity, and project context. Influence was distributed beyond formal leadership, boundary spanning was not determined by seniority, and subgroup expertise and between-group reach reinforced each other. We outline practical applications of SNA for the evaluation and design of scientific teams that aim for knowledge integration across disciplinary boundaries.