Abstract
This works presents a methodology to suggest potential relationships among the elements in the scientific collaboration network. The proposed approach takes into account not only the structure of the relationships among the individuals that constitute the network, but also the content of the information flow propagated in it, modeled from the documents authored by those individuals. The methodology is applied it the accepted papers for the 2nd Workshop on Complex Networks - Complenet’2010. The results show insights on the relationships, both existent and potential, among elements in the network.
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.
Similar content being viewed by others
References
Chang, J., Blei, D.M.: Relational topic models for document networks. In: AISTATS 2009, Clearwater Beach, Florida, USA (2009)
Chang, J., Boyd-Graber, J., et al.: Connections between the lines: augmenting social networks with text. In: KDD 2009, Paris, France (2009)
dos Santos, C.K., Evsukoff, A.G., de Lima, B.S.L.P.: Cluster analysis in document networks. WIT Transactions on Information and Communication Technologies 40, 95–104 (2008)
dos Santos, C.K., Evsukoff, A.G., de Lima, B.S.L.P.: Spectral clustering and community detection in document networks. WIT Transactions on Information and Communication Technologies 42, 41–50 (2009)
Fortunato, S., Barthélemy, M.: Resolution limit in community detection. Proceedings of the National Academy of Sciences 104(1), 36–41 (2007)
Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: CIKM 2003. ACM, New Orleans (2003)
Newman, M.E.J.: Scientific collaboration networks: I. Network construction and fundamental results. Phys. Rev. E 64, 016131 (2001)
Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA 98, 404–409 (2001a)
Newman, M.E.J.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69(2), 26113–26115 (2004)
Ruan, J., Zhang, W.: Identifying network communities with a high resolution. Physical Review E 77(1), 16104–16112 (2008)
Wang, G., Shen, Y., et al.: A vector partitioning approach to detecting community structure in complex networks. Comput. Math. Appl. 55(12), 2746–2752 (2008)
White, S., Smyth, P.: A spectral clustering approach to finding communities in graphs. In: SIAM International Conference on Data Mining, pp. 76–84 (2005)
Xiang, T., Gong, S.: Spectral clustering with eigenvector selection. Pattern Recognition 41(3), 1012–1029 (2008)
Zarei, M., Samani, K.A.: Eigenvectors of network complement reveal community structure more accurately. Physica A 388(8), 1721–1730 (2009)
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
dos Santos, C.K., Onoda, M., Bursztyn, V.S., Bastos, V.M., Fonseca, M.P.A., Evsukoff, A.G. (2011). Potential Link Suggestion in Scientific Collaboration Networks. In: da F. Costa, L., Evsukoff, A., Mangioni, G., Menezes, R. (eds) Complex Networks. Communications in Computer and Information Science, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25501-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-25501-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25500-7
Online ISBN: 978-3-642-25501-4
eBook Packages: Computer ScienceComputer Science (R0)
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.