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Paper Citation Record · LEDGER

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs

As of 11 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2412.17609.

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

pith.paper-citation-record.v1
2412.17609 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:23:48.050833Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T00:50:39.622302Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:09:57.525108Z

Reference resolution

12 of 12 outbound references displayed

  • verified exact1
  • verified fuzzy4
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cd53cc58-6627-45e5-bfae-5737c4c50c9c · outbound

This paper cites Incidence Networks for Geometric Deep Learning.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs Incidence Networks for Geometric Deep Learning

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-11T05:23:48.296558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T05:23:47.947064Z digest=sha256:024126c15140631e14ac8716b1e6251fc09cb6950c5c7092fcf0898978e3b24f

Observation b251d7a9-98fd-4c41-8938-902529e92bf0 · outbound

This paper cites an unresolved cited work.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:23:48.321120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T05:23:48.046619Z digest=sha256:ede30bbe3d409e83f68b75f2baad298e018904916e080ad22d69c49576895f03

Observation 85c2460c-990a-441e-b51e-d3c6720168f8 · outbound

This paper cites These rep- resentations are linearly transformed by component (d), and then summed along with the encoding of the explicit, original node features in output from component (e).

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs These rep- resentations are linearly transformed by component (d), and then summed along with the encoding of the explicit, original node features in output from component (e)

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:23:48.504186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T05:23:48.038834Z digest=sha256:390d1bd7cb40315d8e692aede64e233216f14e5a60dd376a698b34af151dd8a6

Observation 251fc98d-2c88-4f8d-ad7c-fc9e40fbe42e · outbound

This paper cites Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-11T05:23:48.030264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:23:48.030264Z digest=sha256:9803247a9b5ef2fa125c005cfb46f1a6a0f0469e703420364c1fbd93bdf6ad2e

Observation a3816ab5-f8eb-4e8c-bed0-1fe3e1cfb915 · outbound

This paper cites We use the same ‘mae + cosine similarity’ loss (Cant¨ urk et al., 2024).

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs We use the same ‘mae + cosine similarity’ loss (Cant¨ urk et al., 2024)

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:23:48.517959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T05:23:48.034834Z digest=sha256:73422fda7d4672f309a3c857ca524a51748aeebc17fa1738ef0f01c9075e1486

Observation 5c598726-704f-4f78-a35a-778ac576a2ad · outbound

This paper cites in-dataset.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs in-dataset

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:23:48.308886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T05:23:48.050833Z digest=sha256:38a04c6a989a7bd09fe99044de7e2b77d20ba0fe0527014ac16a7f0437a0ea14

Observation ac2cc943-8d69-41b5-9b56-a53f1f8015a1 · outbound

This paper cites In accordance with Cant¨ urk et al.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs In accordance with Cant¨ urk et al

Reference 128

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:23:48.399645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T05:23:48.042843Z digest=sha256:a2d0d8935dc17952aac3ced68e347002eb3ce775b42f30e0affcee24f9ac7c81

Observation 60b3964c-d267-465d-b708-d4f47381c794 · outbound

This paper cites One for All: Towards Training One Graph Model for All Classification Tasks.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs One for All: Towards Training One Graph Model for All Classification Tasks

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-11T05:23:48.025336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:23:48.025336Z digest=sha256:563849d89a786e8844604111771b41d60f03a9c10e7bd60319b1d621a830275d

Observation afe06e31-0cf0-4566-8f9a-56cff09a9548 · outbound

This paper cites Residual Gated Graph ConvNets.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs Residual Gated Graph ConvNets

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-11T05:23:48.009572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:23:48.009572Z digest=sha256:0f6ed89e90f8442670eeffbb332d11e79e01e529d378109e01f5a063b18d69c3

Observation 0afd902a-f44b-4bb3-aba8-db360e21097d · outbound

This paper cites an unresolved cited work.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs Unresolved cited work

Reference 2019

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:23:48.530990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T05:23:48.001151Z digest=sha256:a967d612300d674951056d5dd20f3c05b2bbd1dcb338e2285a6f0b7e98ec2402

Observation ce63fd51-d703-4d56-b8cc-8876e7c4bce5 · outbound

This paper cites Benchmarking Graph Neural Networks.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs Benchmarking Graph Neural Networks

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-11T05:23:48.018034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:23:48.018034Z digest=sha256:0a7841a155c67b6caf96519e302b2c78cb4bf1a607fb374c1997450411871324

Observation 62e823d4-89da-4a91-b6cd-fb038210e18e · outbound

This paper cites PRODIGY: Enabling In-context Learning Over Graphs.

Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs PRODIGY: Enabling In-context Learning Over Graphs

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-11T05:23:48.021354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:23:48.021354Z digest=sha256:085000b458c16c06f1255ff20d16747dc15e3f70fa2d2c79658b6a45e847af7e

Pith citing papers

Observation 68fc3f46-578b-49bc-89f3-895a229fe18f · inbound

Bridging Input Feature Spaces Towards Graph Foundation Models cites this paper.

Bridging Input Feature Spaces Towards Graph Foundation Models Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:41:08.762109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-08T17:18:21.259797Z digest=sha256:728361e172b0079448b8b5f55727aa6cb34fa8aaa61afe1249864259a85b4cc5

Observation d0a57732-d3d1-49bb-acbf-7351c2c5be14 · inbound

A Fair Evaluation of Graph Foundation Models for Node Property Prediction cites this paper.

A Fair Evaluation of Graph Foundation Models for Node Property Prediction Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs

Reference 8

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T16:09:57.527492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-26T00:50:39.622302Z digest=sha256:ecfc7e5f3c6e0a81fb3d8fd94542b5625e2379bd54f7e33006c4ac4eb15463ca