Block dense weighted networks with augmented degree correction

Abstract Dense networks with weighted connections often exhibit a community-like structure, where although most nodes are connected to each other, different patterns of edge weights may emerge depending on node’s community membership. We propose new framework for generating and estimating dense potentially connectivity across communities. The proposed model relies particular class functions which map individual node characteristics the edges connecting those nodes, allowing flexibility while requiring small number parameters relative edges. By leveraging estimation techniques, we also develop bootstrap methodology same set vertices, be useful in circumstances multiple data sets cannot collected. Performance these methods is analyzed theory, simulations, real data.