Towards the analysis of co-authorship networks by iterative spectral partitioning

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 and mine data from real world networks. In this paper, we use a simple spectral decomposition to analyze a co-authorship network. We use a straightforward approach based on algebraic connectivity and characteristic valuation and show that even this simple form of spectral partitioning is useful for the analysis of relations and communities in a co-authorship networks.