arXiv:2606.27455v1 Announce Type: cross Abstract: 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 operato
This paper represents a refinement in the mathematical tools used for understanding complex network dynamics, building on prior work in graph signal processing.
Improved models for inferring network topology from noisy data are crucial for advancing AI and machine learning applications in complex systems, from social networks to biological interactions.
The ability to infer directed graph topology from linear diffusion dynamics with correlated nodal components provides more robust and versatile methods for network analysis than previously available.
- · AI/ML researchers
- · Data scientists
- · Network security
- · Drug discovery
- · Legacy network analysis methods
More accurate understanding of complex system interactions through improved graph inference.
Development of more robust and reliable AI models that operate on relational data.
Enhanced AI applications in fields like cybersecurity, social network analysis, and biological systems modeling.
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