On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates

Understand

In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity.

• Beyond mere adjacency matrices, many real networks also involve vertex covariates that carry key information about underlying block structure in graphs.
• To assess the effects of such covariates on block recovery, we present a comparative analysis of two model-based spectral algorithms for clustering vertices in stochastic blockmodel graphs with vertex covariates.
• The first algorithm uses only the adjacency matrix, and directly estimates the block assignments.