🤖 AI Summary
This work proposes a statistical inference framework based on a hidden Markov network model to accurately estimate subgraph densities and enable joint multi-timepoint comparisons for non-i.i.d. dynamic network sequences subject to observation errors. By explicitly modeling edge-wise observation noise, the method achieves, for the first time, robust inference of subgraph densities across heterogeneous network snapshots and leverages information from multiple time points to enhance estimation efficiency. Theoretical analysis demonstrates that the proposed approach enjoys favorable asymptotic properties in large-scale networks, substantially improving both accuracy and computational efficiency in inferring subgraph structures from noisy dynamic networks.
📝 Abstract
In this work we develop statistical methodology to estimate and perform inference on subgraph densities using time-indexed, or dynamic network sequences. These estimates explicitly adjust for observation errors for the network edges, and have good theoretical properties as the size of the network grows. By specifying a stochastically evolving hidden Markov network model, we address two important directions for further investigation identified by Chang et al. (2022): robustness to non-identical network replicates, and efficient aggregation of multiple available network snapshots. These new methods vastly expand the analysis of noisy networks to new data settings, as network replicates are commonly observed dynamically. The methodology is also extended to consider joint inference for subgraph densities at multiple time points, to facilitate formal statistical comparison of dynamic network snapshots.