Paper Citation Record · LEDGER

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

As of 23 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2411.08550.

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

pith.paper-citation-record.v1
2411.08550 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:35:10.969293Z

measured 23 of 23 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 2026-05-10T18:48:54.370869Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:55:50.316256Z

Reference resolution

22 of 22 outbound references displayed

verified exact4
verified fuzzy11
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 436e5feb-4b4e-462e-8dfe-8e5ac91e4968 · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:10.871263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56786fd9-8fd4-412b-bc40-f824e2eca997 · outbound

This paper cites Demand forecasting using ensemble learning for effective schedul- ing of logistic orders.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Demand forecasting using ensemble learning for effective schedul- ing of logistic orders

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.416888Z

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 38461e1f-1164-4c98-b6d0-9ca80d8fa214 · outbound

This paper cites Geometric deep learning on graphs and manifolds using mixture model cnns.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Geometric deep learning on graphs and manifolds using mixture model cnns

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.400313Z

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 1ae66b3d-68a5-49cb-93ab-9c3d065f8a69 · outbound

This paper cites Topology of International Supply Chain Networks: A Case Study Using Factset Revere Datasets.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Topology of International Supply Chain Networks: A Case Study Using Factset Revere Datasets

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:10.909992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6fbfc3f-7c2c-4276-8875-0ff784baa141 · outbound

This paper cites Simultaneous Decision Making for Stochastic Multi-echelon Inventory Optimization with Deep Neural Networks as Decision Makers.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Simultaneous Decision Making for Stochastic Multi-echelon Inventory Optimization with Deep Neural Networks as Decision Makers

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:35:11.120677Z

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 106cceeb-18aa-4532-9831-cae6d93fe838 · outbound

This paper cites PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.366243Z

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 edffaf5c-0651-4f99-baa9-01e3f4949b26 · outbound

This paper cites Network In Graph Neural Network.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Network In Graph Neural Network

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:35:11.095734Z

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 494be9c7-97ce-4c6d-8c64-8c401d57578a · outbound

This paper cites HRGraph: Leveraging LLMs for HR Data Knowledge Graphs withInformationPropagation-basedJobRecommendation.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks HRGraph: Leveraging LLMs for HR Data Knowledge Graphs withInformationPropagation-basedJobRecommendation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.313497Z

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 f55659fe-f359-4a7d-baf2-4223b1886a14 · outbound

This paper cites GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:10.958038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d562e0a5-c88b-4a3b-b411-5f16668a380a · outbound

This paper cites T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:35:11.050126Z

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 44db43ea-fa96-4793-938d-11c7583d390d · outbound

This paper cites Heterogeneous Spatio-Temporal Graph Convolution Network for Traffic Forecasting with Missing Values.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Heterogeneous Spatio-Temporal Graph Convolution Network for Traffic Forecasting with Missing Values

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.278914Z

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 e481c796-d1b8-4c2c-9a7b-b02d95edd31b · outbound

This paper cites Supply- chain networks: a complex adaptive systems perspective.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Supply- chain networks: a complex adaptive systems perspective

Reference 2005

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.347238Z

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 1b2884d1-07ff-46ad-8dd0-b7b35637f85a · outbound

This paper cites Assessing the vulnerability of supply chains usinggraphtheory.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Assessing the vulnerability of supply chains usinggraphtheory

Reference 2010

Resolution: verified exact; doi, observed 2026-08-12T21:35:11.009193Z

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 fdd9b487-e407-49f1-a8fd-0a8d279eef39 · outbound

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

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.433347Z

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 a6045c1c-3f76-47df-87c3-6102843f50b0 · outbound

This paper cites Neural Message Passing for Quantum Chemistry.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Neural Message Passing for Quantum Chemistry

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:10.883255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48f093e9-15c0-4df6-a2e9-3e6012574b20 · outbound

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

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Relational inductive biases, deep learning, and graph networks

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:10.865682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fffc2bb-3514-43bd-b635-8e1652f263c5 · outbound

This paper cites Demand forecastingusingrandomforestandartificialneuralnetworkforsupplychainmanagement.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Demand forecastingusingrandomforestandartificialneuralnetworkforsupplychainmanagement

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.330472Z

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 bd1d9fec-94e2-444c-9206-66ee08c44d06 · outbound

This paper cites Data analytics in the supplychainmanagement:Reviewofmachinelearningapplicationsindemandforecasting.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Data analytics in the supplychainmanagement:Reviewofmachinelearningapplicationsindemandforecasting

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.452502Z

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 45e7a15e-fa4b-4e9f-a4b1-9f4c984647ea · outbound

This paper cites Data Considerations in Graph Representation Learning for Supply Chain Networks.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Data Considerations in Graph Representation Learning for Supply Chain Networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:10.859707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e08971f9-ac6a-462a-961a-427de88761b3 · outbound

This paper cites Industry classification based on supply chain network information using Graph Neural Networks.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Industry classification based on supply chain network information using Graph Neural Networks

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.296303Z

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 04aca965-0858-4421-a692-1f977fcf88e5 · outbound

This paper cites Graph Artificial Intelligence in Medicine.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Graph Artificial Intelligence in Medicine

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:10.888699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 117be45f-3979-452c-b207-0d5254962d8b · outbound

This paper cites Papajorgji, and Panos M.

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks Papajorgji, and Panos M

Reference 8887

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:11.383733Z

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 d26eee93-5551-47d3-a7bf-97b216324817 · inbound

Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing cites this paper.

Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:55:50.318729Z

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.