Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting

Understand

Latent space models are effective tools for statistical modeling and exploration of network data.

• These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc.
• Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them.
• The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent.