Spectral partitioning is a well known method in the area of graph and matrix analysis. Several approaches based on spectral partitioning and spectral clustering were used to detect structures in real world networks and databases. In this paper, we explore two community detection approaches based on the spectral partitioning to analyze a co-authorship network. The partitioning exploits the concepts of algebraic connectivity and characteristic valuation to form components useful for the analysis of relations and communities in real world social networks. © 2012 IFIP International Federation for Information Processing.
CITATION STYLE
Snášel, V., Krömer, P., Platoš, J., Kudělka, M., & Horák, Z. (2012). On spectral partitioning of co-authorship networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7564 LNCS, pp. 302–313). Springer Verlag. https://doi.org/10.1007/978-3-642-33260-9_26
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