Paper Citation Record · LEDGER

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook

As of 11 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2501.16656.

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

pith.paper-citation-record.v1
2501.16656 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:40:55.620034Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

30 of 30 outbound references displayed

verified exact5
verified fuzzy7
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7152c315-2405-4b95-9a88-759c155cc778 · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.524496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43b1f578-bb11-4443-afa1-3b2a79540ec4 · outbound

This paper cites A short tutorial on the Weisfeiler-Lehman test and its variants.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook A short tutorial on the Weisfeiler-Lehman test and its variants

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:40:56.533799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d772d4cf-3076-4ba6-ab9a-32e984aefcbe · outbound

This paper cites Learning social meta-knowledge for nowcasting human mobility in disaster.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Learning social meta-knowledge for nowcasting human mobility in disaster

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:40:56.520549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cd25e55f-c9e4-4939-9bdc-f25843b5745f · outbound

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

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Semi-Supervised Classification with Graph Convolutional Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.545948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea77b30c-170b-4ab6-9694-1fd043926a15 · outbound

This paper cites RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-10T11:40:56.388920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 50397ae2-45e8-4408-84d6-8a3e2eedce05 · outbound

This paper cites Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.554109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1372ac7d-fe69-4791-8d88-df761b4c9e9a · outbound

This paper cites A Unified Approach to Interpreting Model Predictions.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook A Unified Approach to Interpreting Model Predictions

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.562006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb54fb54-0e40-4667-8941-b8a50c5e9a34 · outbound

This paper cites Traffic4cast at neurips 2022–predict dynamics along graph edges from sparse node data: Whole city traffic and eta from stationary vehicle detectors.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Traffic4cast at neurips 2022–predict dynamics along graph edges from sparse node data: Whole city traffic and eta from stationary vehicle detectors

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:40:56.509325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe8e38be-0119-4177-a8cd-aae4ab35053c · outbound

This paper cites Collaborative imputation of urban time series through cross-city meta-learning.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Collaborative imputation of urban time series through cross-city meta-learning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-08-10T11:40:56.343286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 27957083-bc15-470e-97b3-4f732e54b318 · outbound

This paper cites Physics-Enhanced Graph Neural Networks For Soft Sensing in Industrial Internet of Things.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Physics-Enhanced Graph Neural Networks For Soft Sensing in Industrial Internet of Things

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-10T11:40:55.992560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7627404d-cdca-47f6-8aab-6d2b7f8d130d · outbound

This paper cites Graph Neural Ordinary Differential Equations.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Graph Neural Ordinary Differential Equations

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.580636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba46b98c-40b4-4c48-a9e4-abd8eb34f50a · outbound

This paper cites A Survey of Large Language Models for Graphs.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook A Survey of Large Language Models for Graphs

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-10T11:40:55.955579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 970be5d3-4bf4-46b9-a309-d147d1f57709 · outbound

This paper cites Temporal Graph Networks for Deep Learning on Dynamic Graphs.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.588610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7949abd1-2adf-4d3d-a639-2d76247f3894 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.596091Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0420726-2d28-4052-b591-1c54b018909b · outbound

This paper cites Graph Attention Networks.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Graph Attention Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.600028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fed620ad-3785-4123-9c8a-b2264b9a3a5b · outbound

This paper cites Transformers in Time Series: A Survey.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Transformers in Time Series: A Survey

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.603230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b4c18a8-303b-464e-a947-ed208e77123c · outbound

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

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Graph WaveNet for deep spatial-temporal graph modeling

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:40:56.489659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f1b490a0-daf8-4b20-a15c-3d64174fcc15 · outbound

This paper cites Link representation learning for probabilistic travel time estimation.arXiv preprint arXiv:2407.05895, 2024a.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Link representation learning for probabilistic travel time estimation.arXiv preprint arXiv:2407.05895, 2024a

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-08-10T11:40:55.897475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a7b8eded-6296-4391-9b39-d7579904f10b · outbound

This paper cites A unified dataset for the city-scale traffic assignment model in 20 US cities.Scientific Data, 11(1):325, 2024b.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook A unified dataset for the city-scale traffic assignment model in 20 US cities.Scientific Data, 11(1):325, 2024b

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:40:56.477448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c22eff8-d3ad-42c6-9849-21c60485a048 · outbound

This paper cites Graph Generative Model for Benchmarking Graph Neural Networks.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Graph Generative Model for Benchmarking Graph Neural Networks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.616086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d84e5f6a-3202-4e1f-8df0-776fd3702e71 · outbound

This paper cites Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.620034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82bd4700-573d-484a-9820-b07cfcddb318 · outbound

This paper cites Modeling relational data with graph convolutional networks.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Modeling relational data with graph convolutional networks

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:40:56.499599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0dbdc172-51dc-4832-bfe6-05aa9b9b102f · outbound

This paper cites The Llama 3 Herd of Models.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook The Llama 3 Herd of Models

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.528771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50a69984-79b3-440a-94a8-51e02736c9cc · outbound

This paper cites Gated Graph Sequence Neural Networks.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Gated Graph Sequence Neural Networks

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.558188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d525e668-7229-4c93-a5a5-85bfa0876916 · outbound

This paper cites GPT-4 Technical Report.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook GPT-4 Technical Report

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.508243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bee75c76-0c53-45f4-a1b3-99c828c2d417 · outbound

This paper cites A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.541448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b824611-34db-4b5f-813d-786ade3b08c9 · outbound

This paper cites Relational inductive biases, deep learning, and graph networks.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Relational inductive biases, deep learning, and graph networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.512484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c7e9324-86b1-437b-8ed1-8a06d4fae639 · outbound

This paper cites Explainable Global Wildfire Prediction Models using Graph Neural Networks.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Explainable Global Wildfire Prediction Models using Graph Neural Networks

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.519540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 716d469f-2d0d-4b53-bd17-939c6f89e8a5 · outbound

This paper cites Let Your Graph Do the Talking: Encoding Structured Data for LLMs.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Let Your Graph Do the Talking: Encoding Structured Data for LLMs

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T11:40:55.576876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 581fc667-b261-4ba0-916e-2072bcd3414a · outbound

This paper cites Spectral networks and deep locally connected networks on graphs.

Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook Spectral networks and deep locally connected networks on graphs

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T11:40:56.546851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.