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Paper Citation Record · LEDGER

Virtual Nodes Improve Long-term Traffic Prediction

As of 15 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2501.10048.

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

pith.paper-citation-record.v1
2501.10048 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:27:14.577199Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T01:10:24.579325Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

44 of 44 outbound references displayed

  • verified exact3
  • verified fuzzy26
  • unresolved15
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation b1e16524-8923-491f-8288-613052e14591 · outbound

This paper cites Long-term traffic prediction based on lstm encoder-decoder architecture.

Virtual Nodes Improve Long-term Traffic Prediction Long-term traffic prediction based on lstm encoder-decoder architecture

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.343082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation de083124-267d-429f-b37b-fe3d85ae1e00 · outbound

This paper cites Traffic flow forecasting for urban work zones.

Virtual Nodes Improve Long-term Traffic Prediction Traffic flow forecasting for urban work zones

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.322844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.362862Z digest=sha256:09fcd83158f1f2b9effd4090d6ff6f11d88f6c8cc625ad3d377f3d8a598e6478

Observation dc3aa0d4-9fb2-4d3c-a53e-a56bf6204ba1 · outbound

This paper cites Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting.

Virtual Nodes Improve Long-term Traffic Prediction Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.300986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.368668Z digest=sha256:c39bce544bf4a88dc3719446d9b98e8b03d7ad574c07cee2a68ecf66ca7a5507

Observation 4b495d85-da79-46b6-9470-0136eb7ef261 · outbound

This paper cites On the bottleneck of graph neural networks and its practical implications.

Virtual Nodes Improve Long-term Traffic Prediction On the bottleneck of graph neural networks and its practical implications

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.282980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.374258Z digest=sha256:cd13dcb7a163b024cc982936d68bb81fa0b2b8a24fbcebeb1ec571600c0cb437

Observation 3015992d-dae0-404a-a4bb-feeefcb1218a · outbound

This paper cites Oversquashing in gnns through the lens of information contraction and graph expansion.

Virtual Nodes Improve Long-term Traffic Prediction Oversquashing in gnns through the lens of information contraction and graph expansion

Reference 5

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raw_fallback, observed 2026-08-10T19:27:15.265635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.379315Z digest=sha256:c4d20875cc2626f0581d400fa273253995f9a666cf053cd7811e9342e49f2e4f

Observation 366f515e-cf59-421b-ade2-00c3343c3eb5 · outbound

This paper cites On over-squashing in message passing neural networks: The impact of width, depth, and topology.

Virtual Nodes Improve Long-term Traffic Prediction On over-squashing in message passing neural networks: The impact of width, depth, and topology

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.248439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.384445Z digest=sha256:082da94f869d4b8b87bab682cbcc999d47b1e52c327c68f45f5b05def77df14b

Observation 42048245-e8eb-4cec-86d3-087ce7600907 · outbound

This paper cites Learning Graph-Level Representation for Drug Discovery.

Virtual Nodes Improve Long-term Traffic Prediction Learning Graph-Level Representation for Drug Discovery

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-08-10T19:27:14.733123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.389982Z digest=sha256:384528df6c5a5a6cfdae9796b839c5f2e43c43031d16dfce2467abc25e941d50

Observation 57d7fde0-22d3-4c61-9bb4-60eedd6498e3 · outbound

This paper cites Neural message passing for quantum chemistry.

Virtual Nodes Improve Long-term Traffic Prediction Neural message passing for quantum chemistry

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-10T19:27:14.395587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.395587Z digest=sha256:26a1c82a5ca847ed3b8b746d2a711842223ed817391ac3ccbdc60495ac46b0c0

Observation 539c9b67-c535-4702-8cc0-4424aa9b69cb · outbound

This paper cites Graph Classification via Deep Learning with Virtual Nodes.

Virtual Nodes Improve Long-term Traffic Prediction Graph Classification via Deep Learning with Virtual Nodes

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-10T19:27:14.400917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.400917Z digest=sha256:72ee3cd65abba0b7f338f577143712ddb02414c37ef8560c0c75e6beefaf1ce4

Observation 59ce9ea8-c4de-4dac-be4d-1c443e1c6aac · outbound

This paper cites Boosting graph structure learning with dummy nodes.

Virtual Nodes Improve Long-term Traffic Prediction Boosting graph structure learning with dummy nodes

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.217771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 8fbf204f-6e8f-4d8e-92a5-4a73b49c8134 · outbound

This paper cites The graph neural network model.

Virtual Nodes Improve Long-term Traffic Prediction The graph neural network model

Reference 11

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no resolver link, observed 2026-08-10T19:27:14.412895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.412895Z digest=sha256:8f068eac46b2658f69195187a62b0a8ad942bd2443a97114b56079a88fae8025

Observation 1618c148-c24f-43a0-952a-f640a552bbf9 · outbound

This paper cites Inductive graph neural networks for spatiotemporal kriging.

Virtual Nodes Improve Long-term Traffic Prediction Inductive graph neural networks for spatiotemporal kriging

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.190889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b71392ba-a178-4e21-bc82-31e60555f6a8 · outbound

This paper cites Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks.

Virtual Nodes Improve Long-term Traffic Prediction Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks

Reference 13

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unresolved
no resolver link, observed 2026-08-10T19:27:14.423667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.423667Z digest=sha256:0525eaff80e4de0417fc9221e1583fc05d035bbbb8474d2b7083385269e8f523

Observation 37976db6-91c4-498c-abf8-dd9af9da42cf · outbound

This paper cites Adaptive graph convolutional recurrent network for traffic forecasting.

Virtual Nodes Improve Long-term Traffic Prediction Adaptive graph convolutional recurrent network for traffic forecasting

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-10T19:27:14.428706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.428706Z digest=sha256:d4517d8517f190eb66d3213fa95c3c7702a9f0417c437582fb4d096b6b927b73

Observation 55844b3a-154f-4f80-8af2-1575e273683e · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

Virtual Nodes Improve Long-term Traffic Prediction Highly accurate protein structure prediction with alphafold

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-10T19:27:14.433679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.433679Z digest=sha256:c509d03994fab897ac7dd9b56e624668e0d154a656b1e3ce7c7b0754e2866a44

Observation 5f30eaca-a68c-4f42-9f08-88cc320ff807 · outbound

This paper cites Graph neural networks for social recommendation.

Virtual Nodes Improve Long-term Traffic Prediction Graph neural networks for social recommendation

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.141655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 77e9be9a-f401-46df-81c3-98909ea58856 · outbound

This paper cites Lightgcn: Simplifying and powering graph convolution network for recommendation.

Virtual Nodes Improve Long-term Traffic Prediction Lightgcn: Simplifying and powering graph convolution network for recommendation

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.125636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 325e44c7-a43d-4bd0-ab38-ae3bc4d231cf · outbound

This paper cites Long short-term memory.

Virtual Nodes Improve Long-term Traffic Prediction Long short-term memory

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-10T19:27:14.448545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e58e7e67-466d-4582-abe8-7aeacc2aa7e2 · outbound

This paper cites Attention is all you need.

Virtual Nodes Improve Long-term Traffic Prediction Attention is all you need

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-10T19:27:14.454472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 96e0ed75-e28f-4dee-83f1-93555fc69658 · outbound

This paper cites Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.

Virtual Nodes Improve Long-term Traffic Prediction Diffusion convolutional recurrent neural network: Data-driven traffic forecasting

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 1f30bb9e-e159-49c4-b162-f5d9f2767ce9 · outbound

This paper cites Predicting station-level short-term passenger flow in a citywide metro network using spatiotemporal graph convolutional neural networks.

Virtual Nodes Improve Long-term Traffic Prediction Predicting station-level short-term passenger flow in a citywide metro network using spatiotemporal graph convolutional neural networks

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.067518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 9e1c23a4-155d-4470-9978-1ebdb4a34ba0 · outbound

This paper cites Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting.

Virtual Nodes Improve Long-term Traffic Prediction Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.048434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.469533Z digest=sha256:e374ad33ddd139dec686f1aa27cb62306faaa9810e09b92450d8c53f2ad884bf

Observation 4c4201c9-f3a5-47e3-8f55-6f75489c4294 · outbound

This paper cites Stgat: Spatial-temporal graph attention networks for traffic flow forecasting.

Virtual Nodes Improve Long-term Traffic Prediction Stgat: Spatial-temporal graph attention networks for traffic flow forecasting

Reference 23

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unresolved
no resolver link, observed 2026-08-10T19:27:14.474454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 60cfb17e-062c-496e-8417-f046df7d0f91 · outbound

This paper cites Decoupled dynamic spatial-temporal graph neural network for traffic forecasting.

Virtual Nodes Improve Long-term Traffic Prediction Decoupled dynamic spatial-temporal graph neural network for traffic forecasting

Reference 24

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation c8fda691-0b59-4ef0-8699-473bcef67394 · outbound

This paper cites Learning phrase representations using rnn encoder--decoder for statistical machine translation.

Virtual Nodes Improve Long-term Traffic Prediction Learning phrase representations using rnn encoder--decoder for statistical machine translation

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:15.000847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation ee07b1fd-60a9-4aae-b7dc-d8a9f8ea45a3 · outbound

This paper cites Temporal convolutional networks for action segmentation and detection.

Virtual Nodes Improve Long-term Traffic Prediction Temporal convolutional networks for action segmentation and detection

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:14.982434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation af1bf1a0-84ea-4213-9ab5-a3bc7b98edb4 · outbound

This paper cites Deep multi-view spatial-temporal network for taxi demand prediction.

Virtual Nodes Improve Long-term Traffic Prediction Deep multi-view spatial-temporal network for taxi demand prediction

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:14.963321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 806d830f-e779-42c2-9513-f6939e217245 · outbound

This paper cites Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction.

Virtual Nodes Improve Long-term Traffic Prediction Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:14.940993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 860f8d64-a848-498d-b9fe-82504b4ebc46 · outbound

This paper cites Graph wavenet for deep spatial-temporal graph modeling.

Virtual Nodes Improve Long-term Traffic Prediction Graph wavenet for deep spatial-temporal graph modeling

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:14.920767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.505337Z digest=sha256:6e0edfa65b2bdf58660f673bc7e386f4637df28c1b4516d04aa16e5d44db91f2

Observation c90e71ba-f914-42e9-ba87-4dc03053d3e0 · outbound

This paper cites Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution.

Virtual Nodes Improve Long-term Traffic Prediction Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:14.903425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.510335Z digest=sha256:cfaa4369f8b50b54bfb972bdf2c7bacdf0379a2feece6c3b51752579c0fcba50

Observation 4e30638b-b441-486c-af7a-5fcd6b030d5c · outbound

This paper cites Gman: A graph multi-attention network for traffic prediction.

Virtual Nodes Improve Long-term Traffic Prediction Gman: A graph multi-attention network for traffic prediction

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:14.885369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.515069Z digest=sha256:5eb375274f214b2d314e2409a2801b3c2835aa2e2bfcae32c4fb7b29364f91de

Observation 19e8f914-c2c3-40f7-9c3a-7ce7f9684a82 · outbound

This paper cites Structure-aware transformer for graph representation learning.

Virtual Nodes Improve Long-term Traffic Prediction Structure-aware transformer for graph representation learning

Reference 32

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.519943Z digest=sha256:2e62f397f47098ab1b80c516fb7299c75227b8802d8d0da9e99bd9e5a6ee4ba0

Observation 00d2dded-6917-4682-81b2-6b161b1d398e · outbound

This paper cites Pure transformers are powerful graph learners.

Virtual Nodes Improve Long-term Traffic Prediction Pure transformers are powerful graph learners

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-10T19:27:14.524683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.524683Z digest=sha256:228882b3978bce167eea795981ff6d97085925f9d82f856439e6b58ec028c0f1

Observation e0c22322-ff11-4158-9671-8e60eb7fc0ec · outbound

This paper cites Long range graph benchmark.

Virtual Nodes Improve Long-term Traffic Prediction Long range graph benchmark

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-10T19:27:14.529294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.529294Z digest=sha256:4e3a56565b9ae57640ce8ce53d9db5425531ab346509764a48e7498b557bf688

Observation b973c6db-6ab8-4bba-a830-3a4ff4ca0545 · outbound

This paper cites Representing long-range context for graph neural networks with global attention.

Virtual Nodes Improve Long-term Traffic Prediction Representing long-range context for graph neural networks with global attention

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:14.829382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.533623Z digest=sha256:f72be6174d607fc021c1b8df67a73625fe65186166b3f818a4c2263531d520c0

Observation 2162d20d-e703-4f95-829d-082a521674d5 · outbound

This paper cites Revisiting virtual nodes in graph neural networks for link prediction, 2022.

Virtual Nodes Improve Long-term Traffic Prediction Revisiting virtual nodes in graph neural networks for link prediction, 2022

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:14.811319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 54154535-adb7-4aa4-a875-e5f86be89a89 · outbound

This paper cites Probabilistic Graph Rewiring via Virtual Nodes.

Virtual Nodes Improve Long-term Traffic Prediction Probabilistic Graph Rewiring via Virtual Nodes

Reference 37

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verified exact
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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 34160257-6f88-45c0-aff6-060b34c61c03 · outbound

This paper cites Rewiring with Positional Encodings for Graph Neural Networks.

Virtual Nodes Improve Long-term Traffic Prediction Rewiring with Positional Encodings for Graph Neural Networks

Reference 38

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unresolved
no resolver link, observed 2026-08-10T19:27:14.548019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.548019Z digest=sha256:cc13c465987307d8203a9428d565223c2cf1ce3b7d602594128fc042f203cbda

Observation 69c80129-2f83-4408-9c18-904356506e28 · outbound

This paper cites Probabilistically Rewired Message-Passing Neural Networks.

Virtual Nodes Improve Long-term Traffic Prediction Probabilistically Rewired Message-Passing Neural Networks

Reference 39

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unresolved
no resolver link, observed 2026-08-10T19:27:14.552994Z

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source=arxiv_source observed=2026-08-10T19:27:14.552994Z digest=sha256:cc09d1949d6293a4030b2e7af6390fd08caff45b8eebb2fcfeae4831908a72d5

Observation 1757fef3-f6f9-4ebc-b9a6-b5393c8a70ff · outbound

This paper cites Timegnn: Temporal dynamic graph learning for time series forecasting.

Virtual Nodes Improve Long-term Traffic Prediction Timegnn: Temporal dynamic graph learning for time series forecasting

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:14.791374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.558236Z digest=sha256:34f3a5d6e4fa5a0875fde0bf54fdfb5110c8e6696d3271bb45e858bcfddc51ca

Observation c3370ae3-59fc-4203-846f-d86377f8bd07 · outbound

This paper cites Balanced Graph Structure Learning for Multivariate Time Series Forecasting.

Virtual Nodes Improve Long-term Traffic Prediction Balanced Graph Structure Learning for Multivariate Time Series Forecasting

Reference 41

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verified exact
local_arxiv, observed 2026-08-10T19:27:14.627661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.563059Z digest=sha256:8ee5c3e6bd61b5747175f197d1f6b61aec4603de28bd160564cf55de24071266

Observation debb7e39-367f-46be-86b9-4afb3e1a8aa4 · outbound

This paper cites Connecting the dots: Multivariate time series forecasting with graph neural networks.

Virtual Nodes Improve Long-term Traffic Prediction Connecting the dots: Multivariate time series forecasting with graph neural networks

Reference 42

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unresolved
no resolver link, observed 2026-08-10T19:27:14.567919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.567919Z digest=sha256:2206f5970a319f8b309c86ccbbfd9f631e1975a86f4f747c82affdbb3ef300f3

Observation 83075d91-ba52-411c-ba17-a1e025638908 · outbound

This paper cites Largest: A benchmark dataset for large-scale traffic forecasting.

Virtual Nodes Improve Long-term Traffic Prediction Largest: A benchmark dataset for large-scale traffic forecasting

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-10T19:27:14.572357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T19:27:14.572357Z digest=sha256:50823e395cddd93b353db1f4edb6063bffc0a28acd5f657899f2438575e27c41

Observation 569aaa56-d9d6-408f-b8d2-0c3f7dd2d8d7 · outbound

This paper cites The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains.

Virtual Nodes Improve Long-term Traffic Prediction The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T19:27:14.750531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T19:27:14.577199Z digest=sha256:f009cd8c55f19b05ece3e0a38fed50f9600b72cd0b7fedc615a10a7339fb9f92

Pith citing papers

Observation 003d21bd-5c3a-499b-8a38-cac02b5deff3 · inbound

Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models cites this paper.

Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models Virtual Nodes Improve Long-term Traffic Prediction

Reference 231

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arxiv_id, observed 2026-05-12T01:11:13.412833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-05-12T01:10:24.579325Z digest=sha256:f7cd60c525b76ef03ae5cb7bd2242fc26f92baeaddb46b870b59d50cacbfa9a6