Abstract
Bibliographic databases are a prosperous field for data mining research and social network analysis. The representation and visualization of bibliographic databases as graphs and the application of data mining techniques can help us uncover interesting knowledge regarding how the publication records of authors evolve over time. In this paper we propose a novel methodology to model bibliographical databases as Power Graphs, and mine them in an unsupervised manner, in order to learn basic author types and their properties through clustering. The methodology takes into account the evolution of the co-authorship information, the volume of published papers over time, as well as the impact factors of the venues hosting the respective publications. As a proof of concept of the applicability and scalability of our approach, we present experimental results in the DBLP data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Erten, C., Harding, P.J., Kobourov, S.G., Wampler, K., Yee, G.: Exploring the computing literature using temporal graph visualization. In: Visualization and Data Analysis, pp. 45–56 (2003)
Ke, W., Borner, K., Viswanath, L.: Major information visualization authors, papers and topics in the acm library. In: INFOVIS, pp. 216.1–216.9 (2004)
Li, X., Foo, C., Tew, K., Ng, S.: Searching for rising stars in bibliography networks. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds.) DASFAA 2009. LNCS, vol. 5463, pp. 288–292. Springer, Heidelberg (2009)
Nascimento, M.A., Sander, J., Pound, J.: Analysis of sigmod’s co-authorship graph. SIGMOD Rec. 32, 8–10 (2003)
Royer, L., Reimann, M., Andreopoulos, B., Schroeder, M.: Unraveling protein networks with power graph analysis. PLoS Computational Biology 4(7) (2008)
Smeaton, A.F., Keogh, G., Gurrin, C., McDonald, K., Sødring, T.: Analysis of papers from twenty-five years of sigir conferences: what have we been doing for the last quarter of a century? SIGIR Forum 36, 39–43 (2002)
Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining, pp. 109–110 (2000)
Sun, Y., Wu, T., Yin, Z., Cheng, H., Han, J., Yin, X., Zhao, P.: Bibnetminer: mining bibliographic information networks. In: SIGMOD 2008, pp. 1341–1344. ACM, New York (2008)
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: KDD, pp. 990–998 (2008)
Wang, C., Han, J., Jia, Y., Tang, J., Zhang, D., Yu, Y., Guo, J.: Mining advisor-advisee relationships from research publication networks. In: KDD, pp. 203–212 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tsatsaronis, G. et al. (2011). How to Become a Group Leader? or Modeling Author Types Based on Graph Mining. In: Gradmann, S., Borri, F., Meghini, C., Schuldt, H. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2011. Lecture Notes in Computer Science, vol 6966. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24469-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-24469-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24468-1
Online ISBN: 978-3-642-24469-8
eBook Packages: Computer ScienceComputer Science (R0)