Paper Citation Record · LEDGER

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations

As of 7 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2509.08672.

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

pith.paper-citation-record.v1
2509.08672 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:19:06.855916Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

30 of 30 outbound references displayed

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unresolved30
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malformed identifier0
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External citation measurements

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Outbound references

Observation c63e95f4-e274-4025-a503-9c8d22be88f2 · outbound

This paper cites Zhu,Optimization of power system operation.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Zhu,Optimization of power system operation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:03.338999Z

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Observation 03825e6e-53f3-462a-be46-6ed8b08456a1 · outbound

This paper cites Constrained reinforcement learn- ing for predictive control in real-time stochastic dynamic optimal power flow,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Constrained reinforcement learn- ing for predictive control in real-time stochastic dynamic optimal power flow,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:03.419169Z

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Observation 44a4fddd-169d-42d5-ace9-5efcbcf7c4cd · outbound

This paper cites Modernizing the grid: Challenges and opportunities for a sustainable future,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Modernizing the grid: Challenges and opportunities for a sustainable future,

Reference 3

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Observation f4238ff7-7f5d-4e9f-b6de-320f2a24cb54 · outbound

This paper cites A Review of Safe Reinforcement Learning Methods for Modern Power Systems.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations A Review of Safe Reinforcement Learning Methods for Modern Power Systems

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:03.615935Z

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Observation 04df4c08-155f-4f51-82f9-385b5f80b67e · outbound

This paper cites Complex-value spatio-temporal graph convolutional neural networks and its applications to electric power systems ai,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Complex-value spatio-temporal graph convolutional neural networks and its applications to electric power systems ai,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:03.742531Z

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Observation 6dd9655b-6185-4313-bf95-1871bb65f93a · outbound

This paper cites A transfer learning framework for power system event identification,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations A transfer learning framework for power system event identification,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:03.875910Z

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Observation 71429057-ebe7-4fb5-8761-18c9cfb24457 · outbound

This paper cites Transferable learning of gcn sampling graph data clusters from different power systems,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Transferable learning of gcn sampling graph data clusters from different power systems,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:03.999783Z

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Observation de0bc7d0-44b3-4838-9a0b-f407691fa0a3 · outbound

This paper cites Deep learning in power systems research: A review,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Deep learning in power systems research: A review,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:04.153563Z

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Observation aada8e4f-d3ce-4f6b-aa15-0d2e86274e59 · outbound

This paper cites Transfer learning for transient stability predictions in modern power systems under enduring topolog- ical changes,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Transfer learning for transient stability predictions in modern power systems under enduring topolog- ical changes,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:04.280761Z

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Observation 843ad6b5-4c1b-4503-9489-a7b3d7da4bb5 · outbound

This paper cites A decade survey of transfer learning (2010–2020),.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations A decade survey of transfer learning (2010–2020),

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:04.420962Z

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Observation e4448486-febc-4fb6-beef-3789d1fbb381 · outbound

This paper cites V oltage stability monitoring based on disagreement-based deep learning in a time-varying environment,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations V oltage stability monitoring based on disagreement-based deep learning in a time-varying environment,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:04.559986Z

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Observation 0d7bbe8e-5bca-49ba-8d49-b9a163633e75 · outbound

This paper cites Power system event identification with transfer learning using large-scale real-world synchrophasor data in the united states,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Power system event identification with transfer learning using large-scale real-world synchrophasor data in the united states,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:04.679372Z

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Observation dbda2b47-6dd1-45f0-ad6e-5993d4f34018 · outbound

This paper cites Adaptive assessment of power system transient stability based on active transfer learning with deep belief network,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Adaptive assessment of power system transient stability based on active transfer learning with deep belief network,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:04.829259Z

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Observation 4f3534ef-7a0b-4946-bf86-f59cb35438a4 · outbound

This paper cites An integrated transfer learning method for power system dynamic security assessment of unlearned faults with missing data,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations An integrated transfer learning method for power system dynamic security assessment of unlearned faults with missing data,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:04.934747Z

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Observation 86a4adc2-f8d1-4c7f-8e32-e81ae737c2a5 · outbound

This paper cites Meta-transfer learning-based method for multi-fault analysis and assessment in power system,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Meta-transfer learning-based method for multi-fault analysis and assessment in power system,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:05.086208Z

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Observation 742e2877-27f1-482a-8375-7b106efa96ff · outbound

This paper cites Meta-transfer learning for few-shot learning,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Meta-transfer learning for few-shot learning,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:05.222539Z

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Observation 1d79c186-9e3f-478d-b675-f1e025d96621 · outbound

This paper cites Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:05.349062Z

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Observation 8ceea801-c832-4db9-b146-e2f2ba44995c · outbound

This paper cites Transient stability assessment using deep transfer learning,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Transient stability assessment using deep transfer learning,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:05.463510Z

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Observation 367bf859-81a4-428d-b419-4038595a2df8 · outbound

This paper cites Bidirectional active transfer learning for adaptive power system stability assessment and dominant instability mode identification,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Bidirectional active transfer learning for adaptive power system stability assessment and dominant instability mode identification,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:05.565793Z

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Observation beb12674-27d9-4f2f-9366-60ae51a9426b · outbound

This paper cites A unified deep neural network for solving ac opf in expanding and multiple networks,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations A unified deep neural network for solving ac opf in expanding and multiple networks,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:05.685849Z

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Observation 598e2ac4-e15a-413a-acc5-4190f69017f5 · outbound

This paper cites Spatio-temporal graph convolutional neural networks for physics-aware grid learning algorithms,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Spatio-temporal graph convolutional neural networks for physics-aware grid learning algorithms,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:05.796690Z

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Observation 49f44dca-71f0-4c22-bcd5-78caceecf3aa · outbound

This paper cites Manifold learning: What, how, and why,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Manifold learning: What, how, and why,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:05.928720Z

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Observation 95dd4046-09d6-41d2-986b-7eb6b6ab0db5 · outbound

This paper cites On the challenges of learning with inference networks on sparse, high-dimensional data,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations On the challenges of learning with inference networks on sparse, high-dimensional data,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:05.954854Z

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Observation 04602aa3-f161-40ff-ad44-647b7af07100 · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Semi-supervised classification with graph convolutional networks,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:06.088258Z

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Observation 4094f3e0-5e83-42a1-ba8f-a81717c3eb8f · outbound

This paper cites Attention is all you need,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Attention is all you need,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:06.210423Z

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Observation 78031ee7-ce85-40e7-8e39-23f32e1a58af · outbound

This paper cites Graph attention networks,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Graph attention networks,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:06.295366Z

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Observation cc9e29e8-3661-41c8-8023-b678aa494e22 · outbound

This paper cites Recipe for a General, Powerful, Scalable Graph Trans- former,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Recipe for a General, Powerful, Scalable Graph Trans- former,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:06.421961Z

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Observation f0c802e5-a688-47e4-a6e4-adcbd6d811df · outbound

This paper cites Power system state forecast- ing via deep recurrent neural networks,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Power system state forecast- ing via deep recurrent neural networks,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:06.578976Z

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Observation 0514b63d-4068-4a1f-8633-02e5d7c9e17a · outbound

This paper cites Locational detection of the false data injection attack in a smart grid: A multilabel classification approach,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Locational detection of the false data injection attack in a smart grid: A multilabel classification approach,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:06.717621Z

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Observation e1978cf4-2695-486e-bdbe-13b88a1e563b · outbound

This paper cites Kfrnn: an effective false data injection attack detection in smart grid based on kalman filter and recurrent neural network,.

Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations Kfrnn: an effective false data injection attack detection in smart grid based on kalman filter and recurrent neural network,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T20:19:06.855916Z

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Pith citing papers

No inbound Pith citation observations are available.