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Paper Citation Record · LEDGER

A Fair Comparison of Graph Neural Networks for Graph Classification

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:1912.09893.

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

pith.paper-citation-record.v1
1912.09893 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 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 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:23:13.157177Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:17:29.143256Z

Reference resolution

0 of 0 outbound references displayed

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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 70fe47a1-b535-4bea-8269-b6667e8aba87 · inbound

OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks cites this paper.

OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 19

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verified exact
arxiv_id, observed 2026-05-23T06:12:38.374113Z

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 ee4a167f-8289-4e14-abd6-c5e949735a13 · inbound

Chordless Structure: A Pathway to Simple and Expressive GNNs cites this paper.

Chordless Structure: A Pathway to Simple and Expressive GNNs A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 13

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no resolver link, observed 2026-08-07T14:23:13.157177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:23:13.157177Z digest=sha256:abdc781dac052a95edbcecf98a01992749c233c4909ef41671a8db1fff7c4aaf

Observation c480ffdd-5345-4c16-bf6f-fbcca5235198 · inbound

Heterogeneous Graph Backdoor Attack cites this paper.

Heterogeneous Graph Backdoor Attack A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 7

Resolution
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no resolver link, observed 2026-08-07T12:16:48.827618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:16:48.827618Z digest=sha256:a5f803082d11ea9d9a6054f32ddf618147d36efe04f7fea636c765441b910490

Observation 53531a45-c2b3-41da-8732-53984bd3d847 · inbound

HSG-12M: A Large-Scale Benchmark of Spatial Multigraphs from the Energy Spectra of Non-Hermitian Crystals cites this paper.

HSG-12M: A Large-Scale Benchmark of Spatial Multigraphs from the Energy Spectra of Non-Hermitian Crystals A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 9

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verified exact
arxiv_id, observed 2026-05-19T10:52:15.163040Z

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=pdf_text observed=2026-05-19T10:49:39.186387Z digest=sha256:9ee709925104b3fba5d11c7ddb4ebeb1cc617fc39e3ee10bbeea3989eb3947ff

Observation d63bca2b-e639-46df-9826-f2905df1989c · inbound

HSG-12M: A Large-Scale Benchmark of Spatial Multigraphs from the Energy Spectra of Non-Hermitian Crystals cites this paper.

HSG-12M: A Large-Scale Benchmark of Spatial Multigraphs from the Energy Spectra of Non-Hermitian Crystals A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 9

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verified exact
arxiv_id, observed 2026-05-21T23:54:28.540057Z

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=pdf_text observed=2026-05-21T23:52:40.011346Z digest=sha256:ce74207d191312e42a5ca0c0284f0a438c40d81f342f2d3be8cb4cb50c423a69

Observation 7d9c1a62-20b2-442e-874f-2ed686232905 · inbound

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? cites this paper.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 53

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unresolved
no resolver link, observed 2026-08-07T01:07:34.769782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:07:34.769782Z digest=sha256:cc12d0738239df3c6fe1d3d36b641b54a8fee41b6f11dc00b4c6210a853d5da2

Observation e56e962e-84af-4c34-a844-47b63100c0eb · inbound

Scalable inference of large-scale random kronecker graphs via tensor decomposition and Einstein summation cites this paper.

Scalable inference of large-scale random kronecker graphs via tensor decomposition and Einstein summation A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T22:15:45.422882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:15:45.422882Z digest=sha256:ea71ccfa6a4242a4c7b4245203a8fb5366be5a6e0beb1b4f7a9016b29f5c8aaf

Observation 743a3aaa-d506-4690-8787-17d351d3fc2f · inbound

Bridging Theory and Practice in Link Representation with Graph Neural Networks cites this paper.

Bridging Theory and Practice in Link Representation with Graph Neural Networks A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T21:35:07.884976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:35:07.884976Z digest=sha256:a1b60c3e58423ba3457f2e4138b4f6249657ab904b54b9fba02a9adcbff92a75

Observation 0b0d7efd-ab58-4a52-8a52-376a1f6a11c6 · inbound

Learnable quantum spectral filters for hybrid graph neural networks cites this paper.

Learnable quantum spectral filters for hybrid graph neural networks A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 106

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no resolver link, observed 2026-08-06T19:29:07.383382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:29:07.383382Z digest=sha256:36b2345888f1c1dff062a75c18b6d1a35fcd4292c895d00f82f6dba92fdf6aff

Observation 4c2eb71c-6943-4e38-aff6-c0790d2fb0a0 · inbound

Efficient and Accurate Graph Classification with Hyperdimensional Computing on FPGA cites this paper.

Efficient and Accurate Graph Classification with Hyperdimensional Computing on FPGA A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-21T17:15:25.323678Z

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 ea65eead-1883-400d-a0b0-9f558c06662f · inbound

CTQWformer: A CTQW-based Transformer for Graph Classification cites this paper.

CTQWformer: A CTQW-based Transformer for Graph Classification A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 35

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verified exact
arxiv_id, observed 2026-05-12T06:26:25.752360Z

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 a4144bbf-2593-47f3-8462-1606ff3a182d · inbound

Learning over Positive and Negative Edges with Contrastive Message Passing cites this paper.

Learning over Positive and Negative Edges with Contrastive Message Passing A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 8

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verified exact
arxiv_id, observed 2026-05-20T12:33:16.847741Z

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=pdf_text observed=2026-05-20T12:31:19.969760Z digest=sha256:69f3253839fb615fb993ee5edd4705688577f6cc50092b7cbf7f6d11dde88898

Observation 4f2fd519-1b55-40eb-865f-a10e138317a8 · inbound

AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification cites this paper.

AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-06-29T00:02:49.807565Z

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 4548ee6a-62b9-42df-9abd-fc127815b21d · inbound

Beyond Convolution: Advancing Hypergraph Neural Networks with Hypergraph U-Nets cites this paper.

Beyond Convolution: Advancing Hypergraph Neural Networks with Hypergraph U-Nets A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 60

Resolution
verified exact
arxiv_id, observed 2026-07-03T00:17:29.145164Z

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=pdf_text observed=2026-06-27T17:21:28.096446Z digest=sha256:d825062e5cbdab633257ada16d780666068dcd3993fd1ced1294c13273b96547

Observation 6b3dab5d-5d19-4940-b761-3f4ba06e3397 · inbound

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning cites this paper.

MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 38

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no resolver link, observed 2026-07-31T18:33:46.589286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T18:33:46.589286Z digest=sha256:df92daff9ad7f50f40c84ebd467f302dc6156a01f9841a57745e3417d8e8cd3f

Observation a2bc7e98-a69c-47f7-a36d-fd1a6f4bca3c · inbound

Benchmarking Sheaf Neural Networks for Inductive Tasks cites this paper.

Benchmarking Sheaf Neural Networks for Inductive Tasks A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-04T04:52:31.889191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:52:31.889191Z digest=sha256:9b1c8b48601a228f0428ac6e16eb05e37683bc176b50b393f606c37c462d8975