Paper Citation Record · LEDGER

Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2406.08993.

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

pith.paper-citation-record.v1
2406.08993 v2

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 2026-08-22T06:32:14.747728+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:24:57.483700Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:19:57.661403Z

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 fce21b57-125c-499f-a4bc-f3708f806ef3 · inbound

OpenGU: A Comprehensive Benchmark for Graph Unlearning cites this paper.

OpenGU: A Comprehensive Benchmark for Graph Unlearning Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T22:10:05.392469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf86e3b8-a4b3-417f-8dad-2314b2638a39 · inbound

On the Effectiveness of Random Weights in Graph Neural Networks cites this paper.

On the Effectiveness of Random Weights in Graph Neural Networks Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T19:59:58.786836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c31e959-1938-4139-8d4b-1f6ed8ab2c60 · inbound

IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification cites this paper.

IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T16:13:44.839259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69986199-daf9-4a2a-895b-29023f5ff566 · inbound

Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs cites this paper.

Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:22:16.474900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ef6dcf14-ddb0-4c6a-8159-5e464006715c · inbound

Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective cites this paper.

Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T19:24:57.483700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e830c07-6ab4-4b01-a888-c793b861daef · inbound

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction cites this paper.

Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T19:08:28.244306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7252fc00-2869-4e10-a739-cbe509c7c7e4 · inbound

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models cites this paper.

A Graph Talks, But Who's Listening? Rethinking Evaluations for Graph-Language Models Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T16:47:43.645316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 375ac9b3-89aa-4d6c-93cc-860f39488bda · inbound

Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspective cites this paper.

Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspective Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-04T11:19:46.669755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d14c6d2b-bcf3-416b-9eba-c73bc410bed4 · inbound

Swarm-Inspired Generation of Collective Behaviors in Graph Dynamical Systems cites this paper.

Swarm-Inspired Generation of Collective Behaviors in Graph Dynamical Systems Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T16:19:57.662852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.