Paper Citation Record · LEDGER

Learning the galaxy-environment connection with graph neural networks

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2306.12327.

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

pith.paper-citation-record.v1
2306.12327 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:10:08.564388Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6f436914-bab8-42cf-863c-724cf217ee38 · inbound

Mining for Protoclusters at $z\sim4$ from Photometric Datasets with Deep Learning cites this paper.

Mining for Protoclusters at $z\sim4$ from Photometric Datasets with Deep Learning Learning the galaxy-environment connection with graph neural networks

Reference 119

Resolution: unresolved; no resolver link, observed 2026-08-12T18:10:08.564388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb9e7e0f-86d0-4813-9ff4-c101e3c07a46 · inbound

Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks cites this paper.

Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks Learning the galaxy-environment connection with graph neural networks

Reference 133

Resolution: unresolved; no resolver link, observed 2026-08-09T04:39:47.351867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdb391dc-5a8b-4296-8638-ba9ef91f4dcf · inbound

Cosmology with Topological Deep Learning cites this paper.

Cosmology with Topological Deep Learning Learning the galaxy-environment connection with graph neural networks

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T12:46:17.457613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65da8ad9-05df-4e69-97d8-cfc342fbfe38 · inbound

Explaining ultra-massive quiescent galaxies at $3 < z < 5$ in the context of their environments cites this paper.

Explaining ultra-massive quiescent galaxies at $3 < z < 5$ in the context of their environments Learning the galaxy-environment connection with graph neural networks

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T19:36:50.528239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8099b01-a1ca-4cfb-85e8-abe2d0be2a14 · inbound

Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine learning cites this paper.

Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine learning Learning the galaxy-environment connection with graph neural networks

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-05-17T04:31:31.243553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d2214cd3-21a5-4377-a02e-7ed48a9e478b · inbound

DeepDive: Simultaneous Formation of Massive Quiescent Galaxies in High-Redshift Galaxy Proto-clusters cites this paper.

DeepDive: Simultaneous Formation of Massive Quiescent Galaxies in High-Redshift Galaxy Proto-clusters Learning the galaxy-environment connection with graph neural networks

Reference 134

Resolution: verified exact; arxiv_id, observed 2026-05-09T23:09:26.186084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.