Subsampling of Correlated Graph Signals
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913492817149952 |
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| author | Ravi, Rishabh Majumder, Kaushani Vyas, Kalp Mulleti, Satish |
| author_facet | Ravi, Rishabh Majumder, Kaushani Vyas, Kalp Mulleti, Satish |
| contents | Graph signals are functions of the underlying graph. When the edge-weight between a pair of nodes is high, the corresponding signals generally have a higher correlation. As a result, the signals can be represented in terms of a graph-based generative model. The question then arises whether measurements can be obtained on a few nodes and whether the correlation structure between the signals can be used to reconstruct the graph signal on the remaining nodes. We show that node subsampling is always possible for graph signals obtained through a generative model. Further, a method to determine the number of nodes to select is proposed based on the tolerable error. A correlation-based fast greedy algorithm is developed for selecting the nodes. Finally, we verify the proposed method on different deterministic and random graphs, and show that near-perfect reconstruction is possible with node subsampling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04107 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Subsampling of Correlated Graph Signals Ravi, Rishabh Majumder, Kaushani Vyas, Kalp Mulleti, Satish Signal Processing Graph signals are functions of the underlying graph. When the edge-weight between a pair of nodes is high, the corresponding signals generally have a higher correlation. As a result, the signals can be represented in terms of a graph-based generative model. The question then arises whether measurements can be obtained on a few nodes and whether the correlation structure between the signals can be used to reconstruct the graph signal on the remaining nodes. We show that node subsampling is always possible for graph signals obtained through a generative model. Further, a method to determine the number of nodes to select is proposed based on the tolerable error. A correlation-based fast greedy algorithm is developed for selecting the nodes. Finally, we verify the proposed method on different deterministic and random graphs, and show that near-perfect reconstruction is possible with node subsampling. |
| title | Subsampling of Correlated Graph Signals |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2409.04107 |