Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel

Understand

Spectral clustering is a popular method for community detection in network graphs: starting from a matrix representation of the graph, the nodes are clustered on a low dimensional projection obtained from a truncated spectral decomposition of the matrix.

• Estimating correctly the number of communities and the dimension of the reduced latent space is critical for good performance of spectral clustering algorithms.
• Furthermore, many real-world graphs, such as enterprise computer networks studied in cyber-security applications, often display heterogeneous within-community degree distributions.
• Such heterogeneous degree distributions are usually not well captured by standard spectral clustering algorithms.