Paper Citation Record · LEDGER

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions

As of 8 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2607.16769.

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

pith.paper-citation-record.v1
2607.16769 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T20:03:55.167926Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

24 of 24 outbound references displayed

verified exact0
verified fuzzy0
unresolved24
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fec5f780-f5fb-4122-ae9a-dd0432cde0c7 · outbound

This paper cites GNN-based Probabilistic Supply and Inventory Predictions in Supply Chain Networks.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions GNN-based Probabilistic Supply and Inventory Predictions in Supply Chain Networks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:52.105435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 034dfe6c-b2e9-4509-bdab-60e25722bc96 · outbound

This paper cites Causal dynamic Bayesian networks for simulation metamodeling.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Causal dynamic Bayesian networks for simulation metamodeling

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:52.289670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54a5cf99-1449-44e8-b7df-68d65f32f7ec · outbound

This paper cites an unresolved cited work.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:52.465262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32c56479-b355-4e1c-97bb-b0a94c2092d2 · outbound

This paper cites Enhanced simulation metamodeling via graph and generative neural networks.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Enhanced simulation metamodeling via graph and generative neural networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:52.638927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf2f3591-c55b-4c6c-8f81-a5a4d5e5bb35 · outbound

This paper cites Efficient hybrid simulation optimization via graph neural network meta- modeling.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Efficient hybrid simulation optimization via graph neural network meta- modeling

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:52.724845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52502221-3dc5-4fb6-b14c-064ca1419895 · outbound

This paper cites an unresolved cited work.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:52.872056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce76419a-da1d-478b-b28a-29cb72294d5c · outbound

This paper cites Neural message passing for quantum chemistry.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Neural message passing for quantum chemistry

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:53.015645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 245b1020-c8e8-43dc-ad7b-d8d04e834ced · outbound

This paper cites Research on optimization and management of supply chain networks based on graph neural networks.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Research on optimization and management of supply chain networks based on graph neural networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:53.197323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81914a36-2a8c-4837-96c4-3db57f74faae · outbound

This paper cites Inductive representation learning on large graphs.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Inductive representation learning on large graphs

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:53.314753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e1d6ac2-519b-4138-ba50-aa8a32431eb4 · outbound

This paper cites Erd ˝os goes neural: An unsupervised learning framework for combinatorial optimization on graphs.Advances in Neural Information Processing Systems, 33:6659– 6672, 2020.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Erd ˝os goes neural: An unsupervised learning framework for combinatorial optimization on graphs.Advances in Neural Information Processing Systems, 33:6659– 6672, 2020

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:53.455427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1d093f4-bcce-4fb6-9484-5e6f67e76a5a · outbound

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

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Semi-Supervised Classification with Graph Convolutional Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:53.547166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10b20503-1305-453d-a294-2f3144894137 · outbound

This paper cites DIFFIM: Differentiable influ- ence minimization with surrogate modeling and continuous relaxation.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions DIFFIM: Differentiable influ- ence minimization with surrogate modeling and continuous relaxation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:53.667767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c34b148-dbda-47e5-bfa4-ebdbac0dcb93 · outbound

This paper cites SupplyNetPy Github repository.https://github.com/ SupplyChainSimulation/SupplyNetPy, 2024.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions SupplyNetPy Github repository.https://github.com/ SupplyChainSimulation/SupplyNetPy, 2024

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:53.759573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4b24e79-0f84-4492-8c7b-3ac14b695f15 · outbound

This paper cites An open tool-set for simulation, design-space explo- ration and optimization of supply chains and inventory problems.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions An open tool-set for simulation, design-space explo- ration and optimization of supply chains and inventory problems

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:53.924142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d14f137-e67f-49ed-9873-916d93478fe5 · outbound

This paper cites Development of an open-source library for supply chain modeling and opti- mization.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Development of an open-source library for supply chain modeling and opti- mization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:54.056953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 840d6439-abf7-46f9-b21c-01c2d12aeab4 · outbound

This paper cites Metamodel-based quantile estimation for hedging control of manufacturing systems.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Metamodel-based quantile estimation for hedging control of manufacturing systems

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:54.166595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88c2bd92-5b8a-47c2-9e7a-b438c48f4195 · outbound

This paper cites Combinatorial optimization with physics-inspired graph neural networks.Nature Machine Intelligence, 4(4):367–377, 2022.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Combinatorial optimization with physics-inspired graph neural networks.Nature Machine Intelligence, 4(4):367–377, 2022

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:54.277431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b885b04-3748-456b-b369-8373897e8fb5 · outbound

This paper cites SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:54.361207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cec11f9-eecc-4852-ab69-22cf002af3b7 · outbound

This paper cites Semi-supervised graph convolutional neural network based classification for auto parts inventory management.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Semi-supervised graph convolutional neural network based classification for auto parts inventory management

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:54.505004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aed826f-0e35-4c0a-b9d8-f23698934e52 · outbound

This paper cites Metamodel-assisted sensitivity analysis for controlling the impact of input uncertainty.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Metamodel-assisted sensitivity analysis for controlling the impact of input uncertainty

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:54.620141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e34af851-b858-41a1-9c26-a45559b82966 · outbound

This paper cites DAG-GNN: DAG structure learning with graph neural networks.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions DAG-GNN: DAG structure learning with graph neural networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:54.742801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 601dca3e-981e-4d91-880b-6d6c937ed416 · outbound

This paper cites An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:54.876411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b96f648-eab5-4919-8da9-74015cdd30fa · outbound

This paper cites A machine learning approach for enhancing supply chain visibility with graph-based learning.Supply Chain Analytics, page 100135, 2025.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions A machine learning approach for enhancing supply chain visibility with graph-based learning.Supply Chain Analytics, page 100135, 2025

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:55.031746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 868e5b47-e41f-498e-9e6c-91732efd4eb5 · outbound

This paper cites Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization.

On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T20:03:55.167926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.