Paper Citation Record · LEDGER

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning

As of 15 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2501.10010.

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

pith.paper-citation-record.v1
2501.10010 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:27:40.394282Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

32 of 32 outbound references displayed

verified exact4
verified fuzzy25
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f5fc285b-48a5-4873-b32d-b40835648fb6 · outbound

This paper cites Data augmentation for graph neural networks,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Data augmentation for graph neural networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.508526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cb84f14c-c8a7-446d-ac83-1916ef14ef0f · outbound

This paper cites Graph contrastive learning with adaptive augmentation,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Graph contrastive learning with adaptive augmentation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.481206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f94b7eab-7155-4cbf-a8ab-194da52b1306 · outbound

This paper cites Spectral Feature Augmentation for Graph Contrastive Learning and Beyond.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Spectral Feature Augmentation for Graph Contrastive Learning and Beyond

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:27:40.706986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d2804613-49c8-4ff3-afcf-5cb14039ecd1 · outbound

This paper cites Unleashing the power of graph data augmentation on covariate distribution shift,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Unleashing the power of graph data augmentation on covariate distribution shift,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.458352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5cba2b86-e007-481f-9988-ccd3e4c9c7e3 · outbound

This paper cites Structured sequence modeling with graph convolutional recurrent networks,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Structured sequence modeling with graph convolutional recurrent networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.424374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 630f3c57-c6f8-4600-a8a5-dec5a591ea50 · outbound

This paper cites EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T19:27:40.212362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21cfa016-92dd-42a6-9ddc-55784b769f72 · outbound

This paper cites Dysat: Deep neural representation learning on dynamic graphs via self- attention networks,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Dysat: Deep neural representation learning on dynamic graphs via self- attention networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.396399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0ce69d96-9737-4c9e-9ed1-8eebbcc8337f · outbound

This paper cites Wingnn: Dynamic graph neural networks with random gradient aggregation window,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Wingnn: Dynamic graph neural networks with random gradient aggregation window,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.360605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f8c388d2-653a-411c-8709-d0e26399df0b · outbound

This paper cites Todynet: temporal dynamic graph neural network for multivariate time series classification,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Todynet: temporal dynamic graph neural network for multivariate time series classification,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.333466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1439a607-399b-451f-ac20-ffdf5333e033 · outbound

This paper cites Adaptive data augmentation on temporal graphs,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Adaptive data augmentation on temporal graphs,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.304573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e6a8089a-4375-47a7-bb94-3424c658e14f · outbound

This paper cites Time-aware random walk diffusion to improve dynamic graph learning,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Time-aware random walk diffusion to improve dynamic graph learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.259326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fc4dbdfc-c47f-4ba4-bf1f-c29f734e0fc6 · outbound

This paper cites Temporal graph representation learning with adaptive augmentation contrastive,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Temporal graph representation learning with adaptive augmentation contrastive,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.235735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5ef390a5-bec4-49ad-ba5a-9d51453fe717 · outbound

This paper cites Latent diffusion- based data augmentation for continuous-time dynamic graph model,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Latent diffusion- based data augmentation for continuous-time dynamic graph model,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.168199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 99a1ad52-e08b-4468-a248-629f153cc61b · outbound

This paper cites Rdgsl: Dynamic graph representation learning with structure learning,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Rdgsl: Dynamic graph representation learning with structure learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.133262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4aa46d5a-24fa-4aa8-9980-537355f39940 · outbound

This paper cites Representation learning for dynamic graphs: A survey,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Representation learning for dynamic graphs: A survey,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.086785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation daea6694-7edc-4691-aaeb-57481556afbc · outbound

This paper cites Wavelets on Graphs via Spectral Graph Theory.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Wavelets on Graphs via Spectral Graph Theory

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T19:27:40.289011Z

Source-reported events for the cited work

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Observation 530b3f1f-2a10-4475-9422-0d58b8bf2e1e · outbound

This paper cites Wavelet-based visual analysis of dynamic networks,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Wavelet-based visual analysis of dynamic networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:40.968522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 04d2e266-35d5-4fa3-9017-0587ee096192 · outbound

This paper cites Discrete signal processing on graphs: Frequency analysis,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Discrete signal processing on graphs: Frequency analysis,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:40.943046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c6a0df4c-bdc1-4abf-b259-a1ff31244a00 · outbound

This paper cites Localization in Seeded PageRank.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Localization in Seeded PageRank

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:27:40.621234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation eceb69fd-57ad-4052-a431-97965a278861 · outbound

This paper cites Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:27:40.577320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a73b79c4-de14-4818-aee7-2f108d4c5178 · outbound

This paper cites Edge weight prediction in weighted signed networks,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Edge weight prediction in weighted signed networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:40.917294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f0c63702-c79d-49a7-91ca-137c8316caf1 · outbound

This paper cites Predicting Positive and Negative Links in Online Social Networks.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Predicting Positive and Negative Links in Online Social Networks

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-10T19:27:40.520198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 894ceb4c-3fc4-425b-90c7-ef3dde66e4e3 · outbound

This paper cites Community interaction and conflict on the web,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Community interaction and conflict on the web,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:40.890986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9ac0c04c-ec1a-44c6-aef6-ce15d43e939d · outbound

This paper cites Spatio-temporal attentive rnn for node classification in temporal attributed graphs,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Spatio-temporal attentive rnn for node classification in temporal attributed graphs,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:40.863954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c9048e16-9df5-4109-8d9a-b85aec38fc28 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Semi-Supervised Classification with Graph Convolutional Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T19:27:40.352964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fab0b252-dc27-4a87-a301-e490b167f711 · outbound

This paper cites Roland: graph learning framework for dynamic graphs,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Roland: graph learning framework for dynamic graphs,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:40.840913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cbb334ea-176e-4341-8653-bd0a60e9b2a1 · outbound

This paper cites High-order topology- enhanced graph convolutional networks for dynamic graphs,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning High-order topology- enhanced graph convolutional networks for dynamic graphs,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:40.808342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 15805586-431f-4d57-96fc-2a03d5549c58 · outbound

This paper cites Dergcn: Dynamic-evolving graph convolutional networks for human trajectory prediction,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Dergcn: Dynamic-evolving graph convolutional networks for human trajectory prediction,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:40.779511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b41627c2-88d3-4ad4-b637-e502475a63bc · outbound

This paper cites Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:40.752406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 865b3de4-140b-4284-aadd-80047e6381b6 · outbound

This paper cites Deep graph library: A graph-centric, highly-performant package for graph neural networks,.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Deep graph library: A graph-centric, highly-performant package for graph neural networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:40.729187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8f933dc9-1d3b-49db-9f46-2fd8b173eccc · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 216608194.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Available: https://api.semanticscholar.org/CorpusID: 216608194

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.043033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ee149409-0b61-462c-8cc9-635f19db5dc9 · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID: 262088334.

Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning Available: https://api.semanticscholar.org/CorpusID: 262088334

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:27:41.204276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.