Single Index Latent Variable Models for Network Topology Inference
This addresses network topology inference for complex systems with unmodeled phenomena, but appears incremental as it builds on existing latent variable and regression frameworks.
The paper tackles the problem of inferring network graph structure from data with latent variables by proposing a semi-parametric, non-linear regression model that jointly estimates non-linearities, direct interactions, and indirect effects of unmeasured processes. Experiments show performance on real data, but no concrete numerical results are provided in the abstract.
A semi-parametric, non-linear regression model in the presence of latent variables is applied towards learning network graph structure. These latent variables can correspond to unmodeled phenomena or unmeasured agents in a complex system of interacting entities. This formulation jointly estimates non-linearities in the underlying data generation, the direct interactions between measured entities, and the indirect effects of unmeasured processes on the observed data. The learning is posed as regularized empirical risk minimization. Details of the algorithm for learning the model are outlined. Experiments demonstrate the performance of the learned model on real data.