Paper Citation Record · LEDGER

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation

As of 23 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2505.07674.

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

pith.paper-citation-record.v1
2505.07674 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:12:55.772810Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:32:33.890466Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T18:32:36.750396Z

Reference resolution

19 of 19 outbound references displayed

verified exact2
verified fuzzy10
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e23fcaec-0496-4d86-ba9d-02a0b5784d98 · outbound

This paper cites From statistical-to machine learning-based network traffic prediction,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation From statistical-to machine learning-based network traffic prediction,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:12:56.388548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ed5ef9cb-4633-4fa3-851f-cebe3ab07c83 · outbound

This paper cites Network traffic prediction model considering road traffic parameters using artificial intelligence methods in VANET,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Network traffic prediction model considering road traffic parameters using artificial intelligence methods in VANET,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:12:56.362465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bb36aec9-6089-49d6-b116-e6a6c55a6b1e · outbound

This paper cites Human-Computer Interaction in Smart Devices: Leveraging Sentiment Analysis and Knowledge Graphs for Personalized User Experiences,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Human-Computer Interaction in Smart Devices: Leveraging Sentiment Analysis and Knowledge Graphs for Personalized User Experiences,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:12:56.338209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f7bfa7df-154a-4eb6-b573-74c381c379e4 · outbound

This paper cites Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T22:12:55.649718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4073d223-f369-4a14-877d-3f4769ccc6a2 · outbound

This paper cites Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:12:55.657085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c22425ff-8a34-49af-8d5a-4c90e080e42e · outbound

This paper cites A novel method for improved network traffic prediction using enhanced deep reinforcement learning algorithm,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation A novel method for improved network traffic prediction using enhanced deep reinforcement learning algorithm,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:12:56.314317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9515fe05-83ef-4c37-b2b2-2ccbe2aba1a4 · outbound

This paper cites Digital twin for transportation big data: A reinforcement learning-based network traffic prediction approach,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Digital twin for transportation big data: A reinforcement learning-based network traffic prediction approach,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:12:56.293566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ebef945e-5f8e-402a-9967-18726bb94d29 · outbound

This paper cites Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:12:55.977761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8b914d33-69a5-4da4-86f2-8472d667a622 · outbound

This paper cites Transformer-Based Structural Anomaly Detection for Video File Integrity Assessment,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Transformer-Based Structural Anomaly Detection for Video File Integrity Assessment,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:12:56.270850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7dc23ab7-6047-47d8-bca3-54ca4594e1b3 · outbound

This paper cites A Reinforcement Learning Approach to Traffic Scheduling in Complex Data Center Topologies,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation A Reinforcement Learning Approach to Traffic Scheduling in Complex Data Center Topologies,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:12:55.693479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce1aa86c-69a2-4976-aeaa-b50b0da6f057 · outbound

This paper cites Optimizing Distributed Computing Resources with Federated Learning: Task Scheduling and Communication Efficiency,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Optimizing Distributed Computing Resources with Federated Learning: Task Scheduling and Communication Efficiency,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:12:56.216515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a55b6a00-a881-4167-95b2-726bee864c82 · outbound

This paper cites Dynamic Scheduling Strategies for Resource Optimization in Computing Environments.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Dynamic Scheduling Strategies for Resource Optimization in Computing Environments

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T22:12:55.707793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c00392c-fabc-4de9-983c-b2481107ca01 · outbound

This paper cites Distributed Network Traffic Scheduling via Trust-Constrained Policy Learning Mechanisms,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Distributed Network Traffic Scheduling via Trust-Constrained Policy Learning Mechanisms,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:12:56.189115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 02dd0bad-4c56-4468-812f-435f132a81b3 · outbound

This paper cites A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T22:12:55.732245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fd6ff6f-0869-4092-810b-12e5f6912d1e · outbound

This paper cites Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:12:55.740127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e98c1b16-a526-418b-bb2e-c894c42aae0b · outbound

This paper cites Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T22:12:55.748013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0a87ca1-4c91-4ebf-beee-a88687e485e8 · outbound

This paper cites KST-GCN: A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation KST-GCN: A knowledge-driven spatial-temporal graph convolutional network for traffic forecasting,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:12:56.138959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9a1da54a-cead-48bf-883c-9f5945ff87ce · outbound

This paper cites DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:12:55.840596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 56830d75-f141-4d6e-a4ba-adf8f59bc26c · outbound

This paper cites MTGNN: Multi-task Graph Neural Network based few-shot learning for disease similarity measurement,.

Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation MTGNN: Multi-task Graph Neural Network based few-shot learning for disease similarity measurement,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:12:56.108901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation a8e8ddd5-01d5-4e53-b634-078ccaa3a25a · inbound

Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services cites this paper.

Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:32:36.807581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.