Robust High-Dimensional Covariate-Assisted Network Modeling

Modern network data analysis often involves analyzing network structures alongside covariate features to gain deeper insights into underlying patterns. However, traditional covariate-assisted statistical network models may not adequately handle cases involving high-dimensional covariates, where some covariates could be uninformative or misleading, or the possible mismatch between network and covariate information. To address this issue, we introduce a novel robust high-dimensional covariate-assisted latent space model. This framework links latent vectors representing network structures with simultaneously sparse and low-rank transformations of the high-dimensional covariates, capturing the mutual dependence between network structures and covariates. To robustly integrate this dependence, we use a shrinkage prior on the discrepancy between latent network vectors and low-rank covariate approximation vectors, allowing for potential mismatches between network and covariate information. For scalable inference, we develop two variational inference algorithms, enabling efficient analysis of large-scale sparse networks. We establish the posterior concentration rate within a suitable parameter space and demonstrate how the proposed model facilitates adaptive information aggregation between networks and high-dimensional covariates. Extensive simulation studies and real-world data analyses confirm the effectiveness of our approach.