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Paper Citation Record · LEDGER

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning

As of 7 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.07335.

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

pith.paper-citation-record.v1
2507.07335 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:48:01.291313Z

measured 33 of 33 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

33 of 33 outbound references displayed

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  • verified fuzzy26
  • unresolved7
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d4e712a-189b-421b-83b4-8070c3745884 · outbound

This paper cites write newline.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning write newline

Reference 1

Resolution
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no resolver link, observed 2026-08-06T18:47:58.388273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 781cc67e-4c88-4914-a6bb-46d8f2dffd8b · outbound

This paper cites Constant curvature graph convolutional networks.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Constant curvature graph convolutional networks

Reference 2

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation b5104c42-d424-4cf4-8d41-cec66d395606 · outbound

This paper cites Analyzing the expressive power of graph neural networks in a spectral perspective.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Analyzing the expressive power of graph neural networks in a spectral perspective

Reference 3

Resolution
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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 8bacda96-8f50-4e71-9899-cf2fd084b04e · outbound

This paper cites and Ganea, O.-E.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning and Ganea, O.-E

Reference 4

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 4532f48d-3094-4c31-ad24-b044cab50fbe · outbound

This paper cites Hyperbolic graph convolutional neural networks.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Hyperbolic graph convolutional neural networks

Reference 5

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 9329015f-87f0-4e18-98b9-8562aa2589e4 · outbound

This paper cites Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks

Reference 6

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation a0ad0d4e-e6b8-46db-a87a-77bd276c3246 · outbound

This paper cites Joint adaptive feature smoothing and topology extraction via generalized pagerank gnns.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Joint adaptive feature smoothing and topology extraction via generalized pagerank gnns

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d879878e-e8e5-44c0-a05a-e50432fea858 · outbound

This paper cites Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily, 2022.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily, 2022

Reference 8

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 20b3cfbd-2efe-4212-aca1-eb5569eabc19 · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning A Generalization of Transformer Networks to Graphs

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 042473e5-0f14-40e2-8b49-58d964c665df · outbound

This paper cites E., Weichert, F., and Leskovec, J.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning E., Weichert, F., and Leskovec, J

Reference 10

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 09796dc9-41a5-4317-be90-f4d37388d3a1 · outbound

This paper cites Graphmore: Mitigating topological heterogeneity via mixture of riemannian experts.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Graphmore: Mitigating topological heterogeneity via mixture of riemannian experts

Reference 11

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation a9d0ad1c-c09d-4f8f-aa10-bc53fd3d3c39 · outbound

This paper cites Inductive representation learning on large graphs.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Inductive representation learning on large graphs

Reference 12

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation f25bcbb1-45bf-4e7c-a3c9-1eb51175bdb8 · outbound

This paper cites and Khasahmadi, A.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning and Khasahmadi, A

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation f83ddea4-5666-40f3-af21-2c5e8b50ea16 · outbound

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Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Unresolved cited work

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 23763494-dcfc-42d1-84c6-70daf9c4d5e9 · outbound

This paper cites an unresolved cited work.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Unresolved cited work

Reference 15

Resolution
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no resolver link, observed 2026-08-06T18:47:59.337301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 31496d44-f4af-491c-a6f2-5f68c0577d31 · outbound

This paper cites an unresolved cited work.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Unresolved cited work

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T18:47:59.430279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:47:59.430279Z digest=sha256:ba196d889240ddb6e6f1a4df1247023fb14d039e96184c32f0ff859dbf196f7f

Observation 97bdc387-95a0-4612-b457-379019925ac1 · outbound

This paper cites Rethinking graph transformers with spectral attention.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Rethinking graph transformers with spectral attention

Reference 17

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 6dcfb563-9a3b-425d-8758-26e3363b5b04 · outbound

This paper cites Position: Graph foundation models are already here.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Position: Graph foundation models are already here

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c511b221-5817-4977-a0f2-c5a3f133d531 · outbound

This paper cites Motif-aware riemannian graph neural network with generative-contrastive learning.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Motif-aware riemannian graph neural network with generative-contrastive learning

Reference 19

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 16913230-e25a-431a-b604-bdb2a92671f4 · outbound

This paper cites Learning MLP s on graphs: A unified view of effectiveness and robustness and and efficiency.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Learning MLP s on graphs: A unified view of effectiveness and robustness and and efficiency

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 2eab9973-f79a-449e-9454-8500f491e825 · outbound

This paper cites Graph attention networks.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Graph attention networks

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:03.635626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 10534045-b81f-4ca3-ba76-eed48b241a6c · outbound

This paper cites R., and Wang, Z.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning R., and Wang, Z

Reference 22

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 4f1b0872-f49b-420e-88de-651939b03624 · outbound

This paper cites N., Wang, Z., Nallapati, R., Arnold, A., Xiang, B., Yu, P.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning N., Wang, Z., Nallapati, R., Arnold, A., Xiang, B., Yu, P

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:03.196365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:00.144106Z digest=sha256:63aa2626bceee77758c870a6eb8efde7ca9b8c53dadf616385e42b8ff4f2018f

Observation ec0ed2ed-ed01-4d37-a372-8f6de856f4df · outbound

This paper cites Nodeformer: A scalable graph structure learning transformer for node classification.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Nodeformer: A scalable graph structure learning transformer for node classification

Reference 24

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:00.270381Z digest=sha256:6c4d74f564d9edd3bab0e1a9508795a62ea1e706a7cb257f44c186298585fafa

Observation 579cfe21-f1dd-44c8-9cae-4c8714a013e6 · outbound

This paper cites Difformer: Scalable (graph) transformers induced by energy constrained diffusion.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Difformer: Scalable (graph) transformers induced by energy constrained diffusion

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:02.805058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:00.342739Z digest=sha256:a09e411dcd98c25007a03275d38471584324db8f3ec4f097b6eaf8b2d557c9d3

Observation 6059aa65-dd57-4832-b9a8-c28ec7366a10 · outbound

This paper cites Sgformer: Simplifying and empowering transformers for large-graph representations.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Sgformer: Simplifying and empowering transformers for large-graph representations

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:02.584623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:00.458276Z digest=sha256:bfa931acda10b6992aec3826509112b35edbcf0114449e6d1ad36e6d644d7618

Observation 4da31432-dc86-4565-ae39-13aa0368157c · outbound

This paper cites Graphmetro: Mitigating complex graph distribution shifts via mixture of aligned experts.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Graphmetro: Mitigating complex graph distribution shifts via mixture of aligned experts

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:02.307802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:00.563747Z digest=sha256:cb45c3b4d50dafe80a0b4fb1d4677b5a52fc8b48e62dcafb9e9ba36cd35e68d8

Observation 67700f8c-ae21-40e7-b83b-bf4b55b7b098 · outbound

This paper cites Pseudo-riemannian graph convolutional networks.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Pseudo-riemannian graph convolutional networks

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:02.099669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:00.639602Z digest=sha256:4463b65a958e0ccdb0cb2bb893990ca32109c25e833ef5226bdce9598405f559

Observation d195aa5c-6ba5-4063-b4e2-49db94771130 · outbound

This paper cites Graphsaint: Graph sampling based inductive learning method.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Graphsaint: Graph sampling based inductive learning method

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:01.980087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:00.739591Z digest=sha256:49ddbbeb810de8c8af111d5964657b29639d2314c9a51c8e606e7d251bd0c2c7

Observation 15dd4ea4-0cff-4957-822f-d0fd940c25e9 · outbound

This paper cites Lorentzian graph convolutional networks.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Lorentzian graph convolutional networks

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:01.862737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:00.859754Z digest=sha256:045b7bb94accbf443396d1ad624b9049330b8498dabbea3f13fa0c740ea69ac3

Observation 70035f81-0b75-4d9d-8cc1-7d278de2583a · outbound

This paper cites Graph-Bert: Only Attention is Needed for Learning Graph Representations.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Graph-Bert: Only Attention is Needed for Learning Graph Representations

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:01.074300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:01.074300Z digest=sha256:631320a23f9d125685d4bbde5297c18fa8797f120c57c330d4efc7d303afff4a

Observation 943a5f65-d939-4ce4-99e0-b5acb9c30cbc · outbound

This paper cites Linear attention via orthogonal memory, 2023.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Linear attention via orthogonal memory, 2023

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-06T18:48:01.168647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:48:01.168647Z digest=sha256:30e070fe89624194fbca4bd230ddc8bf9250f5f7a4c33cde5ecb22b8d4d82d66

Observation 79cae2da-5b9f-40f2-a053-3c2b98d2967d · outbound

This paper cites Graph-less neural networks: Teaching old mlps new tricks via distillation.

Leveraging Manifold Embeddings for Enhanced Graph Transformer Representations and Learning Graph-less neural networks: Teaching old mlps new tricks via distillation

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:48:01.592236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-06T18:48:01.291313Z digest=sha256:17ed18ecbbe16817fc31a9ee71b218d84ba452993c2d12bc5929d295cc258afd

Pith citing papers

No inbound Pith citation observations are available.