{"ID":2871556,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10874","arxiv_id":"2509.10874","title":"On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals","abstract":"We investigate graph signal reconstruction and sample selection for classification tasks. We present general theoretical characterisations of classification error applicable to multiple commonly used reconstruction methods, and compare that to the classical reconstruction error. We demonstrate the applicability of our results by using them to derive new optimal sampling methods for linearized graph convolutional networks, and show improvement over other graph signal processing based methods.","short_abstract":"We investigate graph signal reconstruction and sample selection for classification tasks. We present general theoretical characterisations of classification error applicable to multiple commonly used reconstruction methods, and compare that to the classical reconstruction error. We demonstrate the applicability of our...","url_abs":"https://arxiv.org/abs/2509.10874","url_pdf":"https://arxiv.org/pdf/2509.10874v1","authors":"[\"Baskaran Sripathmanathan\",\"Xiaowen Dong\",\"Michael Bronstein\"]","published":"2025-09-13T16:09:43Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\"]","methods":"[]","has_code":false}