Estimating Network Processes via Blind Identification of Multiple Graph Filters

TL;DR

This paper introduces methods to estimate multiple network processes modeled as graph filters from observed outputs driven by a common unknown input, extending classical blind identification to graph-structured data.

Contribution

It proposes novel algorithms for blind identification of multiple graph filters, including least-squares and sparse recovery methods with theoretical guarantees.

Findings

Algorithms successfully recover graph filter coefficients.

Theoretical conditions for accurate recovery are established.

Numerical experiments validate effectiveness and theoretical claims.

Abstract

This paper studies the problem of jointly estimating multiple network processes driven by a common unknown input, thus effectively generalizing the classical blind multi-channel identification problem to graphs. More precisely, we model network processes as graph filters and consider the observation of multiple graph signals corresponding to outputs of different filters defined on a common graph and driven by the same input. Assuming that the underlying graph is known and the input is unknown, our goal is to recover the specifications of the network processes, namely the coefficients of the graph filters, only relying on the observation of the outputs. Being generated by the same input, these outputs are intimately related and we leverage this relationship for our estimation purposes. Two settings are considered, one where the orders of the filters are known and another one where they are not known. For the former setting, we present a least-squares approach and provide conditions for recovery. For the latter scenario, we propose a sparse recovery algorithm with theoretical performance guarantees. Numerical experiments illustrate the effectiveness of the proposed algorithms, the influence of different parameter settings on the estimation performance, and the validity of our theoretical claims.