Using mixed node publication network graphs for analyzing success in interdisciplinary teams

Authors:
André Calero Valdez;Anne Kathrin Schaar;Martina Ziefle;Andreas Holzinger;Sabina Jeschke;Christian Brecher
Affiliations:
Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany;Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany;Human-Computer Interaction Center, RWTH Aachen University, Aachen, Germany;Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Austria;Institute of Information Management in Mechanical Engineering (IMA), Center for Learning and Knowledge Management (ZLW), Assoc. Institute for, Management Cybernetics e.V. (IfU), RWTH Aachen Univer ...;Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, Aachen, Germany
Venue:
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Year:
2012

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Abstract

Large-scale research problems (e.g. health and aging, eonomics and production in high-wage countries) are typically complex, needing competencies and research input of different disciplines [1]. Hence, cooperative working in mixed teams is a common research procedure to meet multi-faceted research problems. Though, interdisciplinarity is --- socially and scientifically --- a challenge, not only in steering cooperation quality, but also in evaluating the interdisciplinary performance. In this paper we demonstrate how using mixed-node publication network graphs can be used in order to get insights into social structures of research groups. Explicating the published element of cooperation in a network graph reveals more than simple co-authorship graphs. The validity of the approach was tested on the 3-year publication outcome of an interdisciplinary research group. The approach was highly useful not only in demonstrating network properties like propinquity and homophily, but also in proposing a performance metric of interdisciplinarity. Furthermore we suggest applying the approach to a large research cluster as a method of self-management and enriching the graph with sociometric data to improve intelligibility of the graph.