Paper Citation Record · LEDGER

Topological Deep Learning: Going Beyond Graph Data

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2206.00606.

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

pith.paper-citation-record.v1
2206.00606 v3

Coverage vector

measured 0 of 0 reference resolution

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measured 31 of 31 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:34:46.900988Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:09:44.409648Z

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External citation measurements

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Outbound references

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Pith citing papers

Observation de1fa20f-b1cd-436c-aaa5-01c0a7520c73 · inbound

Heterogeneous Sheaf Neural Networks cites this paper.

Heterogeneous Sheaf Neural Networks Topological Deep Learning: Going Beyond Graph Data

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T20:58:26.682219Z

Source-reported events for the cited work

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Observation d133467b-1914-4f06-bd68-5d250b669263 · inbound

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics cites this paper.

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics Topological Deep Learning: Going Beyond Graph Data

Reference 37

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Observation 816e55d8-3b98-4ed2-9fd7-1108b3248599 · inbound

Topological Signal Processing and Learning: Recent Advances and Future Challenges cites this paper.

Topological Signal Processing and Learning: Recent Advances and Future Challenges Topological Deep Learning: Going Beyond Graph Data

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:16.159704Z

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Observation 6eaa6ff3-daa4-426a-854b-39e39fd8097c · inbound

Quantum Simplicial Neural Networks cites this paper.

Quantum Simplicial Neural Networks Topological Deep Learning: Going Beyond Graph Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:10.642701Z

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Observation 01c4057f-2fd2-428e-8a74-5899f1e968b0 · inbound

Algebra Unveils Deep Learning -- An Invitation to Neuroalgebraic Geometry cites this paper.

Algebra Unveils Deep Learning -- An Invitation to Neuroalgebraic Geometry Topological Deep Learning: Going Beyond Graph Data

Reference 33

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Observation cafc9aed-8b0e-4e14-94c0-a93948c7d24b · inbound

Enhancing the Utility of Higher-Order Information in Relational Learning cites this paper.

Enhancing the Utility of Higher-Order Information in Relational Learning Topological Deep Learning: Going Beyond Graph Data

Reference 2008

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Observation de8ab074-b86d-4a25-b315-2b181e87f792 · inbound

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning cites this paper.

CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning Topological Deep Learning: Going Beyond Graph Data

Reference 24

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Observation 9fdab32d-2b93-4e64-b6a9-df3880f0fb46 · inbound

Cosmology with Topological Deep Learning cites this paper.

Cosmology with Topological Deep Learning Topological Deep Learning: Going Beyond Graph Data

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:14.473327Z

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Observation 83400e60-b362-4d14-9b92-f0d9172df7c7 · inbound

Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing cites this paper.

Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing Topological Deep Learning: Going Beyond Graph Data

Reference 2020

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Observation 433441d0-aedd-4aa6-a72f-b36faf5886a3 · inbound

Don't be Afraid of Cell Complexes! An Introduction from an Applied Perspective cites this paper.

Don't be Afraid of Cell Complexes! An Introduction from an Applied Perspective Topological Deep Learning: Going Beyond Graph Data

Reference 22

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Observation dbbe5286-a426-4014-b52c-1c6ccf663c5e · inbound

Heat Kernel Goes Topological cites this paper.

Heat Kernel Goes Topological Topological Deep Learning: Going Beyond Graph Data

Reference 36

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Observation 905d1cb3-1482-485c-9259-6795f1e697e3 · inbound

A Sheaf-Theoretic and Topological Perspective on Complex Network Modeling and Attention Mechanisms in Graph Neural Models cites this paper.

A Sheaf-Theoretic and Topological Perspective on Complex Network Modeling and Attention Mechanisms in Graph Neural Models Topological Deep Learning: Going Beyond Graph Data

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T07:10:44.078881Z

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Observation 1a1797f3-1ecd-40ae-aa26-4eab810d97ff · inbound

Root-to-Leaf Path Random Walks, Normalized Hodge Laplacians, and Cheeger Inequalities on Simplicial Complexes cites this paper.

Root-to-Leaf Path Random Walks, Normalized Hodge Laplacians, and Cheeger Inequalities on Simplicial Complexes Topological Deep Learning: Going Beyond Graph Data

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:41:26.534787Z

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Observation 9a29140d-c66f-4838-b24c-79ce40d09165 · inbound

Topological Neural Tangent Kernel cites this paper.

Topological Neural Tangent Kernel Topological Deep Learning: Going Beyond Graph Data

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:41:04.961377Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bebf1396-b2cd-4175-928b-d04d255a1de5 · inbound

TopoU-Net: a U-Net architecture for topological domains cites this paper.

TopoU-Net: a U-Net architecture for topological domains Topological Deep Learning: Going Beyond Graph Data

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:51:28.620025Z

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Observation 9c872d7b-a7e2-4790-8a3f-567de3885980 · inbound

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks cites this paper.

The WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks Topological Deep Learning: Going Beyond Graph Data

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:42:58.778491Z

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Observation 8e543c52-e459-4051-ac70-e6c0949db206 · inbound

Topology-Preserving Neural Operator Learning via Hodge Decomposition cites this paper.

Topology-Preserving Neural Operator Learning via Hodge Decomposition Topological Deep Learning: Going Beyond Graph Data

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T19:07:51.562189Z

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Observation 626a0cbc-0c60-4d04-8711-32197384cb60 · inbound

Topology-Preserving Neural Operator Learning via Hodge Decomposition cites this paper.

Topology-Preserving Neural Operator Learning via Hodge Decomposition Topological Deep Learning: Going Beyond Graph Data

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T21:45:05.609479Z

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Observation a5c14de1-a580-402c-9a13-a2fdef447e03 · inbound

Neural Point-Forms cites this paper.

Neural Point-Forms Topological Deep Learning: Going Beyond Graph Data

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-19T15:02:36.578266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 29a7c3b4-d179-4366-8a1d-b5b39a92915a · inbound

Hypergraph Pattern Machine: Compositional Tokenization for Higher-Order Interactions cites this paper.

Hypergraph Pattern Machine: Compositional Tokenization for Higher-Order Interactions Topological Deep Learning: Going Beyond Graph Data

Reference 20

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Observation 1acbeb9c-5e0f-41e0-a28f-52cb620e8b57 · inbound

Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces cites this paper.

Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces Topological Deep Learning: Going Beyond Graph Data

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:31:14.160250Z

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Observation 19afa3db-542c-4418-a028-17ce28708155 · inbound

Topological Signal Processing: An Application-Oriented Tutorial cites this paper.

Topological Signal Processing: An Application-Oriented Tutorial Topological Deep Learning: Going Beyond Graph Data

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-25T00:15:06.919877Z

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Observation 592ba98c-9d43-40b3-9216-a682fd593bd1 · inbound

Periodic Topological Deep Learning for Polymer Design and Discovery cites this paper.

Periodic Topological Deep Learning for Polymer Design and Discovery Topological Deep Learning: Going Beyond Graph Data

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:23:53.931653Z

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Observation 7d1e2578-b120-47ae-aef8-da052a89e146 · inbound

The Cognitive Categorical Transformer: Category-Theoretic Inductive Biases for Language Modeling cites this paper.

The Cognitive Categorical Transformer: Category-Theoretic Inductive Biases for Language Modeling Topological Deep Learning: Going Beyond Graph Data

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:35:12.597363Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 90669c4f-20b2-4486-a684-7d6518b98fd6 · inbound

Cellular Sheaf Neural Operators for Structure-Preserving Surrogate Modeling of Constrained PDEs cites this paper.

Cellular Sheaf Neural Operators for Structure-Preserving Surrogate Modeling of Constrained PDEs Topological Deep Learning: Going Beyond Graph Data

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-01T20:46:13.288642Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fad50b80-5ed6-4d71-ba84-9396c1362807 · inbound

Topological Neural Operators cites this paper.

Topological Neural Operators Topological Deep Learning: Going Beyond Graph Data

Reference 37

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:17:29.677591Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 780bffd7-ca4e-4f14-aff9-b90d9d08ed8a · inbound

The Heat Kernel Expansion: Curvature for Shock Detection in Higher-Order Financial Networks cites this paper.

The Heat Kernel Expansion: Curvature for Shock Detection in Higher-Order Financial Networks Topological Deep Learning: Going Beyond Graph Data

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T05:49:37.990488Z

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Observation 2108e1f2-37cf-4531-8740-33ca3cc9bcbb · inbound

Collapsed Effective Operators for Higher-order Structures cites this paper.

Collapsed Effective Operators for Higher-order Structures Topological Deep Learning: Going Beyond Graph Data

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T10:09:44.411056Z

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Observation 2ebc0bd9-8610-416b-b9e0-9340c241ecf0 · inbound

Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks cites this paper.

Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks Topological Deep Learning: Going Beyond Graph Data

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-11T23:53:44.613017Z

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Observation 8447c1ec-5987-4af3-9242-0edf8ad0d921 · inbound

New class of exactly flat topological bands - compact localised states protected by local graph topology cites this paper.

New class of exactly flat topological bands - compact localised states protected by local graph topology Topological Deep Learning: Going Beyond Graph Data

Reference 44

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Observation 438c7efe-3d24-4f17-aa3e-ae2de44c5c6c · inbound

Differentiable Lifting for Topological Neural Networks cites this paper.

Differentiable Lifting for Topological Neural Networks Topological Deep Learning: Going Beyond Graph Data

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T00:32:35.844194Z

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Unavailable: canonical work link unavailable.