# Topology Inference over Networks with Nonlinear Coupling

## TL;DR

This paper addresses the challenge of inferring network topology in discrete-time nonlinear stochastic systems by establishing conditions for consistent graph learning, specifically for logistic-type dynamical systems.

## Contribution

It introduces new theoretical conditions enabling accurate topology inference in nonlinear stochastic networks, expanding beyond linear models.

## Key findings

- Conditions for consistent graph inference in nonlinear systems
- Application to logistic-type dynamical systems
- Theoretical framework for topology recovery

## Abstract

This work examines the problem of topology inference over discrete-time nonlinear stochastic networked dynamical systems. The goal is to recover the underlying digraph linking the network agents, from observations of their state-evolution. The dynamical law governing the state-evolution of the interacting agents might be nonlinear, i.e., the next state of an agent can depend nonlinearly on its current state and on the states of its immediate neighbors. We establish sufficient conditions that allow consistent graph learning over a special class of networked systems, namely, logistic-type dynamical systems.