Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting | Paper Citation Record

Observation cb8a070d-499c-421f-9533-aa92f558b1f4 · outbound

This paper cites Discrete graph structure learning for fore- casting multiple time series,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Discrete graph structure learning for fore- casting multiple time series,

Reference 1

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This paper cites Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting,

Reference 2

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This paper cites Learning to remember patterns: Pattern matching memory networks for traffic forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Learning to remember patterns: Pattern matching memory networks for traffic forecasting,

Reference 3

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Observation 7f57dcfe-9dc5-4daa-9a03-43833db05ccb · outbound

This paper cites Spatio-temporal meta-graph learning for traffic forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Spatio-temporal meta-graph learning for traffic forecasting,

Reference 4

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Observation 019488a4-830c-45fd-a127-97dfdaf9eb5d · outbound

This paper cites Deciphering spatio-temporal graph forecasting: A causal lens and treatment,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Deciphering spatio-temporal graph forecasting: A causal lens and treatment,

Reference 5

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Observation f3d57dc4-158c-4e9d-aecc-7246dafd2684 · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Largest: A benchmark dataset for large- scale traffic forecasting,

Reference 6

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Observation eba62f03-c446-4e2d-a602-ccdb78a70381 · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Learning social meta-knowledge for nowcasting human mobility in disaster,

Reference 7

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Observation dc05b8a0-961f-4772-8467-fea27a6981cd · outbound

This paper cites Curb-gan: Conditional urban traffic estimation through spatio-temporal generative adversarial networks,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Curb-gan: Conditional urban traffic estimation through spatio-temporal generative adversarial networks,

Reference 8

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Observation c4b4c571-6965-4993-b6d6-e524704aa8dc · outbound

This paper cites Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learning,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learning,

Reference 9

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Observation 2a9b0ac8-60f5-49f4-af4f-137a2689429e · outbound

This paper cites Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction

Reference 10

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Observation 80f14a94-c4cd-4eb4-a158-adb70e4ba1bc · outbound

This paper cites Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting,

Reference 11

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Observation 0931a703-a50d-4748-a95e-bac088cbdebd · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Graph wavenet for deep spatial-temporal graph modeling,

Reference 12

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Observation 98760402-d778-45db-ba7f-bd280fe4fca1 · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Adaptive graph convolutional recurrent network for traffic forecasting,

Reference 13

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Observation 1c74f27e-6861-4bea-afe3-263ee253298e · outbound

This paper cites Enhancing the ro- bustness via adversarial learning and joint spatial-temporal embeddings in traffic forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Enhancing the ro- bustness via adversarial learning and joint spatial-temporal embeddings in traffic forecasting,

Reference 15

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Observation 28f28dd4-cfe8-443b-bd10-1a8fe33c2299 · outbound

This paper cites Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting,

Reference 16

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Observation 54b69d49-d99b-4f0f-b721-21cc2a31faf2 · outbound

This paper cites Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis

Reference 17

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Observation 663daf51-a473-4099-9429-5bc3ad1c186d · outbound

This paper cites Attention based spatial- temporal graph convolutional networks for traffic flow forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Attention based spatial- temporal graph convolutional networks for traffic flow forecasting,

Reference 18

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Observation 7547d013-4519-4ddc-87d5-2505a848e3e5 · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,

Reference 19

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Observation 2c36cd0a-42c1-4b80-8846-f0fc752c8829 · outbound

This paper cites Dl-traff: Survey and benchmark of deep learning models for urban traffic prediction,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Dl-traff: Survey and benchmark of deep learning models for urban traffic prediction,

Reference 20

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Observation 0395a24f-bf9b-4de6-97d2-b80511d1f4b6 · outbound

This paper cites Freeway performance measurement system: mining loop detector data,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Freeway performance measurement system: mining loop detector data,

Reference 21

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Observation f9f9c2bc-bce0-4758-8bd3-045d7b0e350b · outbound

This paper cites Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting,

Reference 22

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Observation 81b85461-f593-4960-9782-f4d1c2254066 · outbound

This paper cites Attention based spatial- temporal graph convolutional networks for traffic flow forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Attention based spatial- temporal graph convolutional networks for traffic flow forecasting,

Reference 23

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Observation 69fa338b-94c1-44b8-9b85-ec9e5bca5066 · outbound

This paper cites Vision Transformers Need Registers.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Vision Transformers Need Registers

Reference 24

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Observation 117aeccd-58c8-4fff-8f5c-5e93037f290c · outbound

This paper cites A comprehensive survey on transfer learning,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting A comprehensive survey on transfer learning,

Reference 25

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Observation e2e52c68-8f2a-4997-8b1f-78312c29f376 · outbound

This paper cites Online learning: A compre- hensive survey,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Online learning: A compre- hensive survey,

Reference 26

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This paper cites Principal component analysis,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Principal component analysis,

Reference 27

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Observation b222049b-dcd9-4d06-be26-45c0245c7789 · outbound

This paper cites A review of generalized zero-shot learning meth- ods,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting A review of generalized zero-shot learning meth- ods,

Reference 28

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Observation bf1c0d15-ee6b-4b2d-9efb-f1df90ab6e04 · outbound

This paper cites A computer movie simulating urban growth in the detroit region,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting A computer movie simulating urban growth in the detroit region,

Reference 29

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Observation fb7af375-7694-4880-84c8-c005ac35f965 · outbound

This paper cites STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction

Reference 30

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Observation 5bb2fb5e-0816-42a2-8d21-980daefc318a · outbound

This paper cites Spatio-temporal graph convolutional net- works: A deep learning framework for traffic forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Spatio-temporal graph convolutional net- works: A deep learning framework for traffic forecasting,

Reference 31

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Observation ddd4a5e7-5ef2-4d1d-b8cb-8eefd65c3dde · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Decoupled dynamic spatial-temporal graph neural network for traffic forecasting,

Reference 32

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Observation 291f837f-fb33-4ca5-9101-3a033228a5c0 · outbound

This paper cites Coupled layer-wise graph convolution for transportation demand prediction,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Coupled layer-wise graph convolution for transportation demand prediction,

Reference 33

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Observation b9a1cedf-5343-4f0d-a96d-f62cda284646 · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Dynamic and multi- faceted spatio-temporal deep learning for traffic speed forecasting,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.963216Z

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Observation 8fb771ff-0a30-4760-8f4d-8bd18635c08b · outbound

This paper cites Spatial-temporal attention network for crime prediction with adaptive graph learning,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Spatial-temporal attention network for crime prediction with adaptive graph learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.947878Z

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Observation 75dd6bd9-50c3-4439-b5c2-246f6ffee6fc · outbound

This paper cites Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.932445Z

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Observation 0e01aaa4-cc34-44e7-a141-b1d787823330 · outbound

This paper cites Adapgl: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Adapgl: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.916722Z

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Observation d74d1aa7-cb64-47fb-ab77-329ec04b4844 · outbound

This paper cites Freeway performance mea- surement system: operational analysis tool,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Freeway performance mea- surement system: operational analysis tool,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.901172Z

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Observation 33268f4b-ddb5-4365-be8b-8043440a4e4a · outbound

This paper cites A comprehensive survey on graph neural networks,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting A comprehensive survey on graph neural networks,

Reference 39

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Observation 4feee708-a0ab-408a-b8da-17d162968672 · outbound

This paper cites Unist: a prompt-empowered universal model for urban spatio-temporal prediction,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Unist: a prompt-empowered universal model for urban spatio-temporal prediction,

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Observation 5d07cef3-053c-4135-aa20-b602b6afcd95 · outbound

This paper cites Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario

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Resolution: verified exact; local_arxiv, observed 2026-08-12T18:37:07.455317Z

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Observation 089616d9-b741-4473-8430-5a8b4949b380 · outbound

This paper cites Urban traffic prediction from spatio-temporal data using deep meta learning,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Urban traffic prediction from spatio-temporal data using deep meta learning,

Reference 42

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Observation c2608f87-1b3e-485f-b00f-a7f95d233c0f · outbound

This paper cites Cross-City Transfer Learning for Deep Spatio-Temporal Prediction.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Cross-City Transfer Learning for Deep Spatio-Temporal Prediction

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Observation 378ad59b-4a59-4847-9305-5c45d5fd04cd · outbound

This paper cites Analysis of microarray data using z score transformation,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Analysis of microarray data using z score transformation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.851585Z

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Observation 97b4a578-0f5a-48b1-858a-98d1024b004a · outbound

This paper cites Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:37:07.419453Z

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Observation a9605a00-f5a7-4dc8-b5bc-ab59f06fd430 · outbound

This paper cites Con- necting the dots: Multivariate time series forecasting with graph neural networks,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Con- necting the dots: Multivariate time series forecasting with graph neural networks,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T18:37:07.135910Z

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Observation 4a964f47-81ed-4a99-91da-047dcf884403 · outbound

This paper cites Coupled layer-wise graph convolution for transportation demand prediction,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Coupled layer-wise graph convolution for transportation demand prediction,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.836599Z

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Observation ff106827-61e4-4885-9ba2-6d36b53dfc20 · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Dynamic and multi- faceted spatio-temporal deep learning for traffic speed forecasting,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.820308Z

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Observation c03d5df9-e15e-409b-bd26-5cd1fd46d7d0 · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.805187Z

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Observation 60502398-c254-400d-a831-23878309c4df · outbound

This paper cites Spatio-temporal diffusion point processes,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Spatio-temporal diffusion point processes,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.789382Z

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Observation 44d5514e-b890-4a4c-8dd4-2f0965be41b9 · outbound

This paper cites Robust spatiotemporal traffic forecasting with reinforced dynamic adversarial training,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Robust spatiotemporal traffic forecasting with reinforced dynamic adversarial training,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.774517Z

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Observation 83ddfa5c-aa1c-4405-9bb9-80232c331600 · outbound

This paper cites Greto: rem- edying dynamic graph topology-task discordance via target homophily,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Greto: rem- edying dynamic graph topology-task discordance via target homophily,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.759317Z

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Observation 972aba15-6921-4e0e-84e7-407389496cad · outbound

This paper cites Predicting collective human mobility via countering spatiotemporal heterogeneity,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Predicting collective human mobility via countering spatiotemporal heterogeneity,

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Observation 7ca48e36-7bb9-49c8-949f-8622c4a052c2 · outbound

This paper cites Learning gaussian mixture representations for tensor time series forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Learning gaussian mixture representations for tensor time series forecasting,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.734687Z

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Observation 8169d18e-5392-470f-8b15-c050d0e3dd29 · outbound

This paper cites Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:37:07.398763Z

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Observation 1d50c383-b8f1-4a66-b222-e1616c0d18b6 · outbound

This paper cites Utilizing real-world transporta- tion data for accurate traffic prediction,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Utilizing real-world transporta- tion data for accurate traffic prediction,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.719419Z

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Observation a8f2ad13-28d5-4fa1-84f9-af46cc0c4520 · outbound

This paper cites Vector autoregressions,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Vector autoregressions,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T18:37:07.184005Z

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Observation f5e4b016-31e8-42f5-993e-18793a9b4aab · outbound

This paper cites Nonparametric regression and short-term freeway traffic forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Nonparametric regression and short-term freeway traffic forecasting,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.694700Z

Source-reported events for the cited work

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Observation d6f34480-3cfa-4670-80d7-fc32ebd61a78 · outbound

This paper cites A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.679396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4308a4d3-4707-483f-a804-e7bc11e74bb9 · outbound

This paper cites Spectral temporal graph neural network for multivariate time-series forecasting,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Spectral temporal graph neural network for multivariate time-series forecasting,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T18:37:07.197371Z

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Observation bbb4c418-c40e-49d1-a24c-65772d4fd585 · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T18:37:07.201913Z

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Observation b1d54fd5-634e-4da8-aaf1-71cc64f6e7bf · outbound

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

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Semi-Supervised Classification with Graph Convolutional Networks

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T18:37:07.206635Z

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Observation f60da713-6e7c-4dc3-ad73-a3b7c9ccc53c · outbound

This paper cites Graph attention networks,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Graph attention networks,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.652844Z

Source-reported events for the cited work

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Observation cf210c19-c9ea-4df4-bf5b-a5044fdf90c6 · outbound

This paper cites Inductive representation learning on large graphs,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Inductive representation learning on large graphs,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T18:37:07.215596Z

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Observation 57dbcaa7-2149-4454-8c72-978266f81401 · outbound

This paper cites Adaptive universal generalized pagerank graph neural network,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Adaptive universal generalized pagerank graph neural network,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.626185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c6a0e28f-86c4-4b8e-9133-be0ceb02dc83 · outbound

This paper cites Predict then Propagate: Graph Neural Networks meet Personalized PageRank.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T18:37:07.224372Z

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Observation d94ddc8c-dfb1-461c-9b3d-84e98fb2a6f2 · outbound

This paper cites A Comprehensive Survey on Graph Neural Networks.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting A Comprehensive Survey on Graph Neural Networks

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T18:37:07.229329Z

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Observation 8a200f71-3386-44d7-8e3a-08f84859c84f · outbound

This paper cites Shift-robust gnns: Overcoming the limitations of localized graph training data,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Shift-robust gnns: Overcoming the limitations of localized graph training data,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.609974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e3be762f-43f0-4de0-af89-a17610d7ffc9 · outbound

This paper cites Handling distribution shifts on graphs: An invariance perspective,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Handling distribution shifts on graphs: An invariance perspective,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.593094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 10dfe270-c021-40bb-a554-eea3e248d468 · outbound

This paper cites Confidence may cheat: Self-training on graph neural networks under distribution shift,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Confidence may cheat: Self-training on graph neural networks under distribution shift,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.577800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a260a0ce-1345-4eba-8ee5-442d866f0be1 · outbound

This paper cites Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T18:37:07.246322Z

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Observation 918f9a6e-be85-43c9-96eb-d06020f5a4ba · outbound

This paper cites Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.563125Z

Source-reported events for the cited work

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Observation a374d185-08aa-48c3-b630-22f4b2b77f0e · outbound

This paper cites GOOD: A Graph Out-of-Distribution Benchmark.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting GOOD: A Graph Out-of-Distribution Benchmark

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T18:37:07.254963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 310cbb59-efb7-408e-b58b-c3520109574f · outbound

This paper cites Discovering invariant rationales for graph neural networks,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Discovering invariant rationales for graph neural networks,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.547751Z

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Observation 7163e681-97e7-466d-bba4-7d03731ca1a6 · outbound

This paper cites Graph domain adaptation via theory-grounded spectral regularization,.

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting Graph domain adaptation via theory-grounded spectral regularization,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:37:07.532421Z

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