Paper Citation Record · LEDGER

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals

As of 17 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2509.10874.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.10874 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:06:43.751365Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 72bb753c-6ffe-47cf-becb-7e6dea69a3a4 · outbound

This paper cites A graph signal processing framework for the classification of temporal brain data,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals A graph signal processing framework for the classification of temporal brain data,

Reference 1

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Observation bb3b04c9-7eec-4cf1-9c72-5934a4cf8f89 · outbound

This paper cites Big data+ big cities: Graph signals of urban air pollution [exploratory sp],.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Big data+ big cities: Graph signals of urban air pollution [exploratory sp],

Reference 2

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Observation 364db519-58c5-4e1d-8a3d-f4f80607c7bd · outbound

This paper cites Estimating political leanings from mass media via graph-signal restoration with negative edges,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Estimating political leanings from mass media via graph-signal restoration with negative edges,

Reference 3

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Observation 7711b476-926f-4cea-98b1-8245dcfe8468 · outbound

This paper cites String v11: protein–protein association networks with increased cov- erage, supporting functional discovery in genome-wide experimental datasets,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals String v11: protein–protein association networks with increased cov- erage, supporting functional discovery in genome-wide experimental datasets,

Reference 4

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This paper cites Graph signal processing: Overview, challenges, and applications,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Graph signal processing: Overview, challenges, and applications,

Reference 5

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Observation e6db8c9b-61b1-4ced-bb72-1470df1f4fb3 · outbound

This paper cites A-optimal sampling and robust reconstruction for graph signals via truncated neumann series,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals A-optimal sampling and robust reconstruction for graph signals via truncated neumann series,

Reference 6

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Observation 8dd2cda7-326d-4a30-b235-c0271a890873 · outbound

This paper cites Low-complexity graph sampling with noise and signal reconstruction via neumann series,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Low-complexity graph sampling with noise and signal reconstruction via neumann series,

Reference 7

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Observation f2c61d3a-3143-46d4-880f-40bc5866a933 · outbound

This paper cites Signals on graphs: Uncertainty principle and sampling,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Signals on graphs: Uncertainty principle and sampling,

Reference 8

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Observation 90883e8b-0dd4-4f31-aaef-edc523f30531 · outbound

This paper cites Graph sampling with determinantal processes,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Graph sampling with determinantal processes,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:44.471860Z

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Observation 344d4d2d-6410-4142-8c30-a9a71a8f789a · outbound

This paper cites Practical graph signal sampling with log- linear size scaling,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Practical graph signal sampling with log- linear size scaling,

Reference 10

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Source-reported events for the cited work

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Observation f01d4be3-2c6f-4982-8555-721971237e7d · outbound

This paper cites Random sampling of bandlimited signals on graphs,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Random sampling of bandlimited signals on graphs,

Reference 11

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Observation 887d9266-6a0a-4b98-8b7d-ac3b19b115c3 · outbound

This paper cites Graph-based signal sampling with adaptive subspace reconstruction for spatially-irregular sensor data,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Graph-based signal sampling with adaptive subspace reconstruction for spatially-irregular sensor data,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:44.420427Z

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Observation bff4f4d7-ed12-4e69-a18d-3afda9e07eeb · outbound

This paper cites Fast graph sampling set selection using gershgorin disc alignment,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Fast graph sampling set selection using gershgorin disc alignment,

Reference 13

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Observation 5ac6dbcb-e99f-4f5f-b7db-1f5c8f4c0786 · outbound

This paper cites Discrete signal processing on graphs: Sampling theory,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Discrete signal processing on graphs: Sampling theory,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:44.386820Z

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Observation 14cdd0a5-e8f6-4826-8ecf-d697ee084a56 · outbound

This paper cites Pukelsheim,Optimal design of experiments.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Pukelsheim,Optimal design of experiments

Reference 15

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Observation fa59e175-011c-4d7f-8f84-78621cf57c29 · outbound

This paper cites Graph learning from incomplete graph signals: From batch to online methods,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Graph learning from incomplete graph signals: From batch to online methods,

Reference 16

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Source-reported events for the cited work

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Observation bcf7c9f5-c717-4921-806a-4e0ff9a48059 · outbound

This paper cites Efficient graph learning from noisy and incomplete data,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Efficient graph learning from noisy and incomplete data,

Reference 17

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Observation ea374f40-8990-4de3-baf3-bb3ea4aa960c · outbound

This paper cites Towards joint graph learning and sampling set selection from data,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Towards joint graph learning and sampling set selection from data,

Reference 18

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Observation ce8c5810-26c2-4e59-b631-e6006fa1d53b · outbound

This paper cites Sampling in paley-wiener spaces on combinatorial graphs,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Sampling in paley-wiener spaces on combinatorial graphs,

Reference 19

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Observation 3112e84c-c48c-43d3-a86e-c6c8bc8e9eff · outbound

This paper cites Near-optimality of greedy set selection in the sampling of graph signals,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Near-optimality of greedy set selection in the sampling of graph signals,

Reference 20

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Observation 8a4f28e4-da7a-499c-848e-0b3a781fb38a · outbound

This paper cites Greedy sampling of graph signals,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Greedy sampling of graph signals,

Reference 21

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Observation c5f6f87c-123a-4354-a43c-adf6027fc5c6 · outbound

This paper cites Sampling large data on graphs,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Sampling large data on graphs,

Reference 22

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Observation 74b9b2d9-9f2b-4204-b161-ce5e9a1a9f1a · outbound

This paper cites Signal recovery on graphs: Fundamental limits of sampling strategies,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Signal recovery on graphs: Fundamental limits of sampling strategies,

Reference 23

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Observation 89a0605b-bc92-49ca-b445-b38e0785fc3e · outbound

This paper cites Learning with local and global consistency,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Learning with local and global consistency,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:44.220979Z

Source-reported events for the cited work

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Observation 93b6a2e2-acf4-4632-86dd-ae695fce3ceb · outbound

This paper cites Incomplete Graph Representation and Learning via Partial Graph Neural Networks.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Incomplete Graph Representation and Learning via Partial Graph Neural Networks

Reference 25

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Observation b34545e0-f1eb-4c33-b878-a04161529617 · outbound

This paper cites Learning on attribute-missing graphs,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Learning on attribute-missing graphs,

Reference 26

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Observation fb8955f1-c017-4e50-b40b-ad88cbb63085 · outbound

This paper cites Matrix Completion on Graphs.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Matrix Completion on Graphs

Reference 27

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Observation 42d4182b-8e3a-4873-816f-f52ca4f19a91 · outbound

This paper cites Missing data imputation with adversarially-trained graph convolutional networks,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Missing data imputation with adversarially-trained graph convolutional networks,

Reference 28

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Observation 8b3001aa-7b86-49a5-ad2a-3f38e8d89982 · outbound

This paper cites On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features

Reference 29

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On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Graph convolutional networks for graphs containing missing features,

Reference 30

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Observation 71a08034-c33a-4f9f-9c8f-639adfab8780 · outbound

This paper cites Handling missing data with graph representation learning,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Handling missing data with graph representation learning,

Reference 31

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Observation 43265b3f-21e1-42fe-a291-ec20c9d45e03 · outbound

This paper cites On the impact of sample size in reconstructing noisy graph signals: A theoretical characterisation,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals On the impact of sample size in reconstructing noisy graph signals: A theoretical characterisation,

Reference 32

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Observation 80197ac8-5db0-421f-898f-2118ba353796 · outbound

This paper cites Learning lapla- cian matrix in smooth graph signal representations,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Learning lapla- cian matrix in smooth graph signal representations,

Reference 33

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On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Simplifying graph convolutional networks,

Reference 34

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On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Convolutional neural networks on graphs with fast localized spectral filtering,

Reference 35

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This paper cites Graph neural networks with learnable and optimal polynomial bases,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Graph neural networks with learnable and optimal polynomial bases,

Reference 36

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Observation 243c98a7-06e4-4fd8-b5bf-d007db2f6d1a · outbound

This paper cites On manipulating signals of user-item graph: A jacobi polynomial-based graph collaborative filtering,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals On manipulating signals of user-item graph: A jacobi polynomial-based graph collaborative filtering,

Reference 37

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Observation be91c1a3-8189-48ff-94c0-f07e6d79b56e · outbound

This paper cites Bernnet: Learning arbitrary graph spectral filters via bernstein approximation,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Bernnet: Learning arbitrary graph spectral filters via bernstein approximation,

Reference 38

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On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Unresolved cited work

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This paper cites Classic GNNs are strong baselines: Reassessing GNNs for node classification,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Classic GNNs are strong baselines: Reassessing GNNs for node classification,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:44.044483Z

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Observation e523d8a3-ae70-4af7-82e6-50cce58b328e · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks,.

On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Understanding the difficulty of training deep feedforward neural networks,

Reference 41

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On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Gaussian processes on graphs via spectral kernel learning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:44.018711Z

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Observation a956d0d6-cb78-4512-be54-8b248d6968ac · outbound

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On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals Inductive representation learning on large graphs,

Reference 43

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Source-reported events for the cited work

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Pith citing papers

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