Abstract
The CAiSE community has always prided itself as more than just a normal conference – a successful social network with a very special culture. In this chapter, we apply formal social network analysis to study this community and its evolution of its first quarter-centennial of existence. Using a methodology and dataset developed for an analysis of Computer Science as a whole, we demonstrate the unusual positioning of CAiSE as a quasi-interdisciplinary conference between several sub-disciplines of Computer Science. We show that under an evolution model developed in our research CAiSE pursues a very successful and promising path, and we identify key topics and key players among the CAiSE authors. As the social network analysis focusses on formal aspects such as co-authorship and citations, we unfortunately must leave out one of the undoubtedly most critical success factors: the fun of being in the CAiSE community.
Chapter PDF
Similar content being viewed by others
References
Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. In International AAAI Conference on Weblogs and Social Media, pages 361–362.
Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. J. ACM, 46:604–632.
Lave, J. and Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Learning in Doing. Cambridge University Press.
Leskovec, J., Kleinberg, J., and Faloutsos, C. (2005). Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, KDD ’05, pages 177–187, New York, NY, USA. ACM.
McCallum, A., Nigam, K., and Ungar, L. H. (2000). Efficient clustering of high-dimensional data sets with application to reference matching. In KDD ’00: Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 169–178, New York, NY, USA. ACM.
Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proc.Natl.Acad.Sci.USA, 98:404.
Newman, M. E. J. (2004). Fast algorithm for detecting community structure in networks. Physical Review E, 69:066133.
Page, L., Brin, S., Motwani, R., and Winograd, T. (1998). The pagerank citation ranking: Bringing order to the web. Technical report;, Stanford University.
Pham, M. and Klamma, R. (2010). The structure of the computer science knowledge network. In 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 17–24.
Pham, M. C. (2013). Dynamic Social Network Analysis and Recommender Technologies in Scientific Communities: The Case of Computer Science. PhD thesis, RWTH Aachen University, Aachen – Germany.
Pham, M. C., Derntl, M., Klamma, R., and Jarke, M. (2012). Development patterns of scientific communities in technology enhanced learning. Educational Technology and Society, 15(3):323–335.
Pham, M. C., Klamma, R., and Jarke, M. (2011). Development of computer science disciplines: a social network analysis approach. Social Netw. Analys. Mining, 1(4):321–340.
Wenger, E., McDermott, R., and Snyder, W. (2002). Cultivating communities of practice: a guide to managing knowledge. Harvard Business School Press.
Acknowledgements
This work is supported by the DFG-funded excellence cluster UMIC, the B-IT Research School, and EU Integrated Project Layers.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Jarke, M., Pham, M.C., Klamma, R. (2013). Evolution of the CAiSE Author Community: A Social Network Analysis. In: Bubenko, J., Krogstie, J., Pastor, O., Pernici, B., Rolland, C., Sølvberg, A. (eds) Seminal Contributions to Information Systems Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36926-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-36926-1_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36925-4
Online ISBN: 978-3-642-36926-1
eBook Packages: Computer ScienceComputer Science (R0)