Life-cycles and mutual effects of scientific communities

Procedia - Social and Behavioral Sciences

Volumn 22, Issue , 2011, Pages 37-48

  Belák, Václav ^a   Karnstedt, Marcel ^a   Hayes, Conor ^a  

^a NATIONAL UNIVERSITY OF IRELAND   (Ireland)

Author keywords

Co citation analysis; Community life cycle; Cross community dynamics; Graph mining; SNA

Indexed keywords

EID: 80054097135     PISSN: 18770428     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.sbspro.2011.07.054     Document Type: Conference Paper

References (24)

1. Baeza-Yates, R., Mika, P., & Zaragoza, H. (2008). Search, web 2.0, and the semantic web. IEEE Intelligent Systems, 23, (pp. 80-82).
2. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, P10008.
3. Bollen, J., Van de Sompel, H., Hagberg, A., & Chute, R. (2009). A principal component analysis of 39 scientific impact measures. PloS one, 4.
4. Bordea, G. (2010). The semantic web: research and applications. chapter Concept extraction applied to the task of expert finding. (pp. 451-456). Springer
5. Springer. Chi, Y., Song, X., Zhou, D., Hino, K., & Tseng, B. L. (2007). Evolutionary spectral clustering by incorporating temporal smoothness. In KDD '07 (pp. 153-162).
6. Delvenne, J.-C., Yaliraki, S. N., & Barahona, M. (2010). Stability of graph communities across time scales. (pp. 12755-12760). volume 107.
7. Everett, M., & Borgatti, S. (1999). The centrality of groups and classes. The Journal of Mathematical Sociology, 23, (pp. 181-201).
8. Friedkin, N. (1998). A structural theory of social influence. Cambridge Univ. Press.
9. Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Softw. Pract. Exper., 21, (pp. 1129-1164).
10. Gmür, M. (2003). Co-citation analysis and the search for invisible colleges: A methodological evaluation. Scientometrics, 57, (pp. 27-57).
11. Greene, D., Archambault, D., & Cunningham, P. (2010a). TextLuas: tracking and visualizing document and term clusters in dynamic text data. http://mlg.ucd.ie/textluas.
12. Greene, D., Doyle, D., & Cunningham, P. (2010b). Tracking the evolution of communities in dynamic social networks. In ASONAM'10.
13. Hayes, C., Avesani, P., & Bojars, U. (2007). LNAI: Advances in Web Mining. chapter An Analysis of Bloggers, Topics and Tags for a Blog Recommender System. Springer.
14. Karnstedt, M., & Hayes, C. (2009). Towards cross-community effects in scientific communities. In KDML'09 (pp. 52-55).
15. Kuhn, T. S. (1996). The Structure of Scientific Revolutions. University Of Chicago Press.
16. Newman, M. (2010). Networks: an introduction. Oxford Univ. Press.
17. Palla, G., Barabási, A.-L., & Vicsek, T. (2007). Quantifying social group evolution. Nature, 446, (pp. 664-667).
18. Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14, (pp. 130-137).
19. Rosvall, M., & Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. National Academy of Sciences USA, 105, (pp. 1118-1123).
20. Small, H. (2006). Tracking and predicting growth areas in science. Scientometrics, 68, (pp. 595-610).
21. Sulo, R., Berger-Wolf, T., & Grossman, R. (2010). Meaningful selection of temporal resolution for dynamic networks. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs MLG '10 (pp. 127-136). New York, NY, USA: ACM.
22. Tantipathananandh, C., Berger-Wolf, T., & Kempe, D. (2007). A framework for community identification in dynamic social networks. In KDD '07 (pp. 717-726).
23. Upham, S., & Small, H. (2010). Emerging research fronts in science and technology: patterns of new knowledge development. Scientometrics, 83, (pp. 15-38).
24. Wakita, K., & Tsurumi, T. (2007). Finding community structure in a mega-scale social networking service. In WWW/Internet'07 (pp. 153-162).