Paper Citation Record · LEDGER

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting

As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2608.09082.

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

pith.paper-citation-record.v1
2608.09082 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:58:56.738206Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact2
verified fuzzy35
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e0f86056-de64-4558-9bf1-b59f59cc4efd · outbound

This paper cites Nested spatio-temporal time series fore- casting.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Nested spatio-temporal time series fore- casting

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.411581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d32a65ab-d086-48eb-8546-331d3ce94beb · outbound

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

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Spectral temporal graph neural network for multivariate time-series forecasting

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.397872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 00a24a43-c163-42de-a932-6d8328be5486 · outbound

This paper cites Impact of noisy supervision in foundation model learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(7):5690–5707, 2025.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Impact of noisy supervision in foundation model learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(7):5690–5707, 2025

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.383313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 80871a7f-a67b-4ac3-986f-7a4d12167fe4 · outbound

This paper cites Prompt federated learning for weather forecasting: Toward foundation models on meteorological data.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Prompt federated learning for weather forecasting: Toward foundation models on meteorological data

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.368472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bb23a184-0b65-4019-8f4e-0b3989536cbe · outbound

This paper cites Hypercomplex prompt-aware multi- modal recommendation.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Hypercomplex prompt-aware multi- modal recommendation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.354604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4e868df-ab3d-4ac2-aba2-69ee3d49e291 · outbound

This paper cites FedGCR: Achieving perfor- mance and fairness for federated learning with distinct client types via group customization and reweighting.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting FedGCR: Achieving perfor- mance and fairness for federated learning with distinct client types via group customization and reweighting

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.340912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4d605cfb-1d5b-49e1-85a2-11c4fd422489 · outbound

This paper cites Graph neural controlled differential equa- tions for traffic forecasting.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Graph neural controlled differential equa- tions for traffic forecasting

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.326571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f4cc94e5-8d81-419c-95b8-4bcec5c77ddc · outbound

This paper cites SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting SpoT-Mamba: Learning Long-Range Dependency on Spatio-Temporal Graphs with Selective State Spaces

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T23:58:56.578323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f272719f-c718-4056-a588-54018786caab · outbound

This paper cites Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, and Salman A.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, and Salman A

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.313367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 499c2d83-fe68-48f7-8c51-33d429146672 · outbound

This paper cites Pdg2seq: Periodic dynamic graph to sequence model for traffic flow prediction.Neural Netw., 183(C), 2025.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Pdg2seq: Periodic dynamic graph to sequence model for traffic flow prediction.Neural Netw., 183(C), 2025

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.299460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c1d27a52-9088-4c13-a37a-810baddd75b3 · outbound

This paper cites Mozhgan Rahmatinia, and Seyed- Amin Hosseini-Seno.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Mozhgan Rahmatinia, and Seyed- Amin Hosseini-Seno

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.285830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 91c9b162-1fb0-4f2b-95b7-288ce2bb92cf · outbound

This paper cites Pdformer: Propagation delay-aware dynamic long- range transformer for traffic flow prediction.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Pdformer: Propagation delay-aware dynamic long- range transformer for traffic flow prediction

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.271908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0de97c6a-5503-4983-bf55-e3228d91db1e · outbound

This paper cites Graph neural network for traffic forecasting: The research progress.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Graph neural network for traffic forecasting: The research progress

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.257983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 095a626e-9e0e-4566-910d-5d0c0e463bc0 · outbound

This paper cites Fedgraph-fair: Federated learning with per- sonalization and fairness via dynamic graphs and distribu- tionally robust optimization.Information Sciences, 728: 122710, 2026.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Fedgraph-fair: Federated learning with per- sonalization and fairness via dynamic graphs and distribu- tionally robust optimization.Information Sciences, 728: 122710, 2026

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.244034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c70bbede-8de2-4a7f-ac0e-5e87793fd866 · outbound

This paper cites Lightcts: A lightweight framework for correlated time series forecasting.Proceedings of the ACM on Management of Data, 1(2):1–26, 2023.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Lightcts: A lightweight framework for correlated time series forecasting.Proceedings of the ACM on Management of Data, 1(2):1–26, 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.229976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 56520b80-c6f1-4cb9-92c3-fbaecfe20f20 · outbound

This paper cites Enhancing topolog- ical dependencies in spatio-temporal graphs with cycle mes- sage passing blocks.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Enhancing topolog- ical dependencies in spatio-temporal graphs with cycle mes- sage passing blocks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.215801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 18a78d52-0d85-41f6-a7ff-9c8024aa62d7 · outbound

This paper cites STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T23:58:56.620316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd8bf4fa-3db8-4906-8f40-eefce0a96c43 · outbound

This paper cites Model- contrastive federated learning.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Model- contrastive federated learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.201828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 948589c1-3f68-4144-b52b-a3117ba7bd07 · outbound

This paper cites Towards understanding camera motions in any video.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Towards understanding camera motions in any video

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.187122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 00f89193-81c1-486f-b318-edf66feac972 · outbound

This paper cites Building a precise video language with human-AI oversight.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Building a precise video language with human-AI oversight

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.172415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8e6b2f82-3001-4d3a-9f09-cd84c60eaf7f · outbound

This paper cites A general spatio-temporal backbone with scalable contextual pattern bank for urban continual forecasting.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting A general spatio-temporal backbone with scalable contextual pattern bank for urban continual forecasting

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.157643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 14250179-6a69-4079-928d-2206d6314c2b · outbound

This paper cites Spatio-temporal adaptive embedding makes vanilla transformer sota for traf- fic forecasting.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Spatio-temporal adaptive embedding makes vanilla transformer sota for traf- fic forecasting

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.141967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f28bf00f-ef26-47c9-ad4d-3e4e9c6e8e1d · outbound

This paper cites Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:58:56.837045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1725bbbf-8795-4af3-9d4a-c030d1e7d963 · outbound

This paper cites Personalized federated learning for spatio-temporal forecasting: A dual semantic alignment-based contrastive ap- proach.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Personalized federated learning for spatio-temporal forecasting: A dual semantic alignment-based contrastive ap- proach

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.126966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0a7d8dd7-b208-4a3c-a0d8-54afeeb4a42b · outbound

This paper cites Communication- Efficient Learning of Deep Networks from Decentralized Data.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Communication- Efficient Learning of Deep Networks from Decentralized Data

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.112188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8eb13a72-eda7-42d0-aa14-01df75939186 · outbound

This paper cites FedProc: Prototypical Contrastive Federated Learning on Non-IID data.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting FedProc: Prototypical Contrastive Federated Learning on Non-IID data

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-11T23:58:56.815079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6f77b545-7224-4ff9-accf-66c3ad6caf00 · outbound

This paper cites Fair- ness in federated learning: Trends, challenges, and opportu- nities.Advanced Intelligent Systems, 2025.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Fair- ness in federated learning: Trends, challenges, and opportu- nities.Advanced Intelligent Systems, 2025

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.097560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6caadbfe-c307-412a-ac7f-f8712ef64e32 · outbound

This paper cites Federated spatial-temporal traffic forecasting with vmd-enhanced graph attention and lstm.Scientific Re- ports, 16(1):8852, 2026.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Federated spatial-temporal traffic forecasting with vmd-enhanced graph attention and lstm.Scientific Re- ports, 16(1):8852, 2026

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.082792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5b6d5219-1267-42fd-ba02-1548b7cac0f0 · outbound

This paper cites Efraimidis.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Efraimidis

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.067796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 83cee66f-c35a-4014-b1d4-c3fbb4e5c8db · outbound

This paper cites Adaptive federated optimization.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Adaptive federated optimization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.052945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b608364a-80fa-4a95-ab21-91cd50eb6a5b · outbound

This paper cites Federated Optimization in Heterogeneous Networks.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Federated Optimization in Heterogeneous Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T23:58:56.683954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 880f2287-1c89-4b78-b87e-07a5993bbce3 · outbound

This paper cites Modeling multivariate biosignals with graph neural networks and structured state space.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Modeling multivariate biosignals with graph neural networks and structured state space

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.037934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 40aca073-6891-4dfe-a120-92fdcd3886d3 · outbound

This paper cites Federated graph learning under domain shift with generalizable proto- types.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Federated graph learning under domain shift with generalizable proto- types

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.022700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5d136df5-f1f7-48b8-b07c-1a8cbf619775 · outbound

This paper cites Unlocking dy- namic inter-client spatial dependencies: A federated spatio- temporal graph learning method for traffic flow forecasting.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Unlocking dy- namic inter-client spatial dependencies: A federated spatio- temporal graph learning method for traffic flow forecasting

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:57.008398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4f67af7b-c277-45d7-aeab-752cc3af54f1 · outbound

This paper cites AirShot: Efficient few-shot detection for autonomous explo- ration.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting AirShot: Efficient few-shot detection for autonomous explo- ration

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:56.992750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 32bcca35-17ec-47a6-8e97-4cf0628ae5d8 · outbound

This paper cites A decomposition dynamic graph con- volutional recurrent network for traffic forecasting.Pattern Recognition, page 109670, 2023.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting A decomposition dynamic graph con- volutional recurrent network for traffic forecasting.Pattern Recognition, page 109670, 2023

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:56.976630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a4f6e8a-bb1b-4a63-ae1e-f2e6d8303265 · outbound

This paper cites an unresolved cited work.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:58:56.961652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 21d5918e-c467-41b0-9400-5ba4dc15d150 · outbound

This paper cites an unresolved cited work.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-11T23:58:56.944931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9bf18cea-7f53-4b99-b4b2-6bbf405414d2 · outbound

This paper cites Multi-cali any- thing: Dense feature multi-frame structure-from-motion for large-scale camera array calibration.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Multi-cali any- thing: Dense feature multi-frame structure-from-motion for large-scale camera array calibration

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:56.929866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b2ed7180-9a62-4497-a655-781e0ac9c574 · outbound

This paper cites Dual attention-based federated learning for wireless traffic prediction.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Dual attention-based federated learning for wireless traffic prediction

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:56.914650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2e46554d-278d-450e-9c1d-ee75a8028004 · outbound

This paper cites Subgraph federated learning with missing neighbor generation.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Subgraph federated learning with missing neighbor generation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:56.898774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6c17d55d-6924-4854-ab04-3c383c205680 · outbound

This paper cites Graph Neural Networks: A Review of Methods and Applications.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Graph Neural Networks: A Review of Methods and Applications

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T23:58:56.733118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b2f0ca1-6b9f-4534-8b46-215e44966a2b · outbound

This paper cites Multispans: A multi-range spatial-temporal trans- former network for traffic forecast via structural entropy op- timization.

F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting Multispans: A multi-range spatial-temporal trans- former network for traffic forecast via structural entropy op- timization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:58:56.883270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.