Applying Social Network Analysis and Centrality Measures to Improve Information Flow Analysis

  • Stephan Kiesling
  • Jil Klünder
  • Diana Fischer
  • Kurt Schneider
  • Kai Fischbach
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10027)


In software development projects, documents are very important for sharing requirements and other information among employees. However, information can be transported in different ways. Conversations, meetings, workshops and emails convey and impart information as well. Especially large companies struggle in dealing with unclear and incorrect information flows. These information flows can be improved by means of information flow analysis and flow patterns. One technique to analyze information flows is the FLOW method. It supports visualization and analysis of information flows to detect lacks and anomalies and thereby improves information flows. An analyst gathers information transported in the company. Afterwards, information flows are visualized and analyzed based on patterns and personal experience. Nevertheless, analysis based on individual knowledge is error-prone. Hence, we improve the FLOW method with the help of social network analysis applying centrality measures to the FLOW method and to support the FLOW analyst.



This work was supported by the German Federal Ministry of Education and Research under grant number K3: FKZ 13N13548 (2015-2018) and by the German Research Foundation (DFG) under grant number 263807701 (Project TeamFLOW, 2015-2017).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Stephan Kiesling
    • 1
  • Jil Klünder
    • 1
  • Diana Fischer
    • 2
  • Kurt Schneider
    • 1
  • Kai Fischbach
    • 2
  1. 1.Software Engineering GroupLeibniz Universität HannoverHannoverGermany
  2. 2.University of BambergBambergGermany

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