Single Index Latent Variable Models for Network Topology Inference

TL;DR

This paper introduces a semi-parametric model that infers network topology by jointly estimating non-linear relationships, direct interactions, and effects of unmeasured factors using regularized empirical risk minimization, demonstrated on real data.

Contribution

It presents a novel semi-parametric approach for network inference that accounts for latent variables and non-linearities, advancing existing methods.

Findings

Effective in modeling complex network interactions

Successfully applied to real-world data

Improves understanding of unmeasured influences

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.