Directed Graph Topology Inference via Graph Filter Identification
We address the problem of inferring a directed network from nodal measurements generated by linear diffusion dynamics on the sought graph. Observations are modeled as the outputs of a graph convolutional filter, i.e., a polynomial (with unknown coefficients) of a local diffusion graph-shift operator encoding the latent graph topology, excited with an ensemble of independent graph signals with arbitrarily-correlated nodal components. Unlike prior efforts that considered undirected graphs and white signal excitations, here the graph-shift operator and the observations' covariance matrix are not simultaneously diagonalizable. In this challenging context, we first rely on measurements of the ...
| Authors | Rasoul Shafipour, Andrei Buciulea, Santiago Segarra et al. |
|---|---|
| Primary category | stat.ML |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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