Paper Citation Record · LEDGER

Do Transformers Really Perform Bad for Graph Representation?

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2106.05234.

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

pith.paper-citation-record.v1
2106.05234 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T06:48:42.383801Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T08:14:25.509869Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 99b8764a-c9fa-4d4f-b167-957774df0e74 · inbound

Deep sequence models tend to memorize geometrically; it is unclear why cites this paper.

Deep sequence models tend to memorize geometrically; it is unclear why Do Transformers Really Perform Bad for Graph Representation?

Reference 199

Resolution: verified exact; arxiv_id, observed 2026-05-21T20:40:36.215132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fedd473d-7d17-4bc9-b17f-3c9a76639b66 · inbound

How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations cites this paper.

How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations Do Transformers Really Perform Bad for Graph Representation?

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:20:22.924818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b56f66a0-af55-4264-a01b-4fccfca49396 · inbound

Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks cites this paper.

Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks Do Transformers Really Perform Bad for Graph Representation?

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:10:43.149032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 098bf0c5-617e-44a6-b250-bd1fdcd9c9ef · inbound

Graph Transformers and Stabilized Reinforcement Learning for Large-Scale Dynamic Routing Modulation and Spectrum Allocation in Elastic Optical Networks cites this paper.

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-21T00:03:51.947146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 661c0c92-d291-46bc-85d7-3de833ebfe33 · inbound

GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model cites this paper.

GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model Do Transformers Really Perform Bad for Graph Representation?

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:31:08.967739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8d10ee1b-3681-4218-807d-68d63a359973 · inbound

Closed-Loop Molecular Design with Calibrated Deference cites this paper.

Closed-Loop Molecular Design with Calibrated Deference Do Transformers Really Perform Bad for Graph Representation?

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-29T09:23:16.447030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 800a80fa-d29c-4737-8340-23f47f25d2a4 · inbound

GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem cites this paper.

GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem Do Transformers Really Perform Bad for Graph Representation?

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:14:25.512873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fe190ce2-5af1-44ff-851f-bf7ef9b224e9 · inbound

Graph Neural Networks for the Graphical Bootstrap cites this paper.

Graph Neural Networks for the Graphical Bootstrap Do Transformers Really Perform Bad for Graph Representation?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T04:50:25.686477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b171a37-4d07-46c5-9a5e-72b4436c3e26 · inbound

QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization cites this paper.

QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization Do Transformers Really Perform Bad for Graph Representation?

Reference 180

Resolution: unresolved; no resolver link, observed 2026-08-01T06:48:42.383801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.