Integrated Representation and Visualisation of the Dynamics in Computer-mediated Social Networks

In this paper we will demonstrate the potential of processing and visualising the dynamics of computer-mediated communities by means of Social Network Analysis. According to the fact that computer-mediated community systems are manifested also as structured data, we use data structures like e-mail, discussion boards, and bibliography sources for an automatic transformation into social network data formats. Currently our developed converter DMD (Data Multiplexer Demultiplexer) supports GraphML, UCINET, and Pajek formats besides our own data formats which are used for real-time analysis of CSCL (Computer Supported Collaborative Learning) activities. In the case of communication data our converters utilize conversation graphs reflecting aspects of speech act and conversational theory to produce directed graphs in the cases where one-mode person networks are desired. The paper will demonstrate a 3-dimensional visualisation of an author community based on Bibtex bibliography data converted into GraphML. Based on this dataset we visualise publications network with a tool called Weaver, which is developed in our research group. According to Lothar Krempel’s algorithm, Weaver uses the first two dimensions to embed the network structure within a common solution space. The third dimension is used for representing the time axis and thus the dynamics of co-authorship relations.Concluding we aim to discuss potential issues and problems of our approach and the possibilities especially concerning the appropriate visualisation and segmentation of long term communications, such as mailing lists.

[1]  Csr Young,et al.  How to Do Things With Words , 2009 .

[2]  David Leake,et al.  Modeling and Using Context, 5th International and Interdisciplinary Conference, CONTEXT 2005, Paris, France, July 5-8, 2005, Proceedings , 2005, CONTEXT.

[3]  Alejandra Martínez-Monés,et al.  Studying participation networks in collaboration using mixed methods , 2006, Int. J. Comput. Support. Collab. Learn..

[4]  Vladimir Batagelj,et al.  Pajek - Analysis and Visualization of Large Networks , 2001, Graph Drawing Software.

[5]  Thierry Chanier,et al.  How Social Network Analysis can help to Measure Cohesion in Collaborative Distance-Learning , 2003, CSCL.

[6]  Andreas Harrer,et al.  Ontology Facilitated Community Navigation - Who Is Interesting for What I Am Interested in? , 2005, CONTEXT.

[7]  Kai Hakkarainen,et al.  Patterns of Interaction in Computer-Supported Learning: A Social Network Analysis , 2000 .

[8]  Michael Jünger,et al.  Graph Drawing Software , 2003, Graph Drawing Software.

[9]  Heinz Ulrich Hoppe,et al.  Building bridges within learning communities through ontologies and "thematic objects" , 2005, CSCL.

[10]  Matthias Trier,et al.  IT-Supported Visualization of Knowledge Community Structures , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[11]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[12]  Julita Vassileva,et al.  Social Visualization Encouraging Participation in Online Communities , 2006, CRIWG.

[13]  Bethany S. Dohleman.  Exploratory social network analysis with Pajek , 2006 .

[14]  Dorothea Wagner,et al.  A Hybrid Model for Drawing Dynamic and Evolving Graphs , 2005, GD.

[15]  Lothar Krempel,et al.  Visualisierung komplexer Strukturen - Grundlagen der Darstellung mehrdimensionaler Netzwerke , 2005 .

[16]  Niels Pinkwart,et al.  Evaluation of communication in web-supported learning communities ? an analysis with triangulation research design , 2006, Int. J. Web Based Communities.