The paper derives feedback conditions that violate topology identifiability for partial and full observations and proposes a distributed design that trades consensus deviation against topology privacy under limited budgets.
Graph signal processing: History, development, impact, and outlook
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
An unsupervised learnable spectral filtering method separates graph signals from a single mixture by reconstructing each source in its own low-frequency Laplacian subspace.
Tutorial on TSP foundations via the combinatorial Hodge Laplacian with an illustrative application to edge signals in brain imaging data.
citing papers explorer
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Preserving Topology Privacy of Network Systems by Feedback: Conditions and Distributed Design
The paper derives feedback conditions that violate topology identifiability for partial and full observations and proposes a distributed design that trades consensus deviation against topology privacy under limited budgets.
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Graph Signal Separation with Learnable Spectral Filters
An unsupervised learnable spectral filtering method separates graph signals from a single mixture by reconstructing each source in its own low-frequency Laplacian subspace.
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Topological Signal Processing: An Application-Oriented Tutorial
Tutorial on TSP foundations via the combinatorial Hodge Laplacian with an illustrative application to edge signals in brain imaging data.