Paper Citation Record · LEDGER

3D Infomax improves GNNs for Molecular Property Prediction

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2110.04126.

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

pith.paper-citation-record.v1
2110.04126 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:08:46.256928Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:26:24.165225Z

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 6f72a143-8e9e-45f7-8e57-21452dbcfc58 · inbound

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study cites this paper.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study 3D Infomax improves GNNs for Molecular Property Prediction

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-08T15:08:46.256928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e83eaee-48c9-4c89-ae57-864b8ca0a96d · inbound

Attention-based graph neural networks: a survey cites this paper.

Attention-based graph neural networks: a survey 3D Infomax improves GNNs for Molecular Property Prediction

Reference 170

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:26:24.975131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 41692d35-c028-4fed-a5a6-f40702ebda21 · inbound

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution cites this paper.

DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution 3D Infomax improves GNNs for Molecular Property Prediction

Reference 174

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:26:24.167003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a69412e-949a-45f9-a155-4da6489b52ba · inbound

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density cites this paper.

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density 3D Infomax improves GNNs for Molecular Property Prediction

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T22:26:38.433263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.