Identifying the Topology of Undirected Networks From Diffused Non-Stationary Graph Signals

Identifying the Topology of Undirected Networks From Diffused Non-Stationary Graph Signals

We address the problem of inferring an undirected graph from nodal observations, which are modeled as non-stationary graph signals generated by local diffusion dynamics that depend on the structure of the unknown network. Using the so-called graph-shift operator (GSO), which is a matrix representati...

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Bibliographic Details
Main Authors: Rasoul Shafipour, Santiago Segarra, Antonio Marques, Gonzalo Mateos
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Network topology inference
graph learning
graph signal processing
(non-)stationary graph processes
network diffusion
system identification
Online Access:https://ieeexplore.ieee.org/document/9369883/

Internet

https://ieeexplore.ieee.org/document/9369883/

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