Paper Citation Record · LEDGER

Graph Condensation for Graph Neural Networks

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2110.07580.

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

pith.paper-citation-record.v1
2110.07580 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:05:43.731953Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:26:56.812714Z

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 cdd2f190-e4b6-4400-9e02-5e61efd8e025 · inbound

FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening cites this paper.

FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening Graph Condensation for Graph Neural Networks

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:23:22.041044Z

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 e6334290-46f1-4eda-8d62-9be135827917 · inbound

Random Walk Guided Hyperbolic Graph Distillation cites this paper.

Random Walk Guided Hyperbolic Graph Distillation Graph Condensation for Graph Neural Networks

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T14:05:43.731953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64ce550f-a974-446e-a706-8f633905e4ab · inbound

GCAL: Adapting Graph Models to Evolving Domain Shifts cites this paper.

GCAL: Adapting Graph Models to Evolving Domain Shifts Graph Condensation for Graph Neural Networks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T15:00:23.867413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57e5060a-1552-4f29-8fea-e31d56e74d14 · inbound

Simple yet Effective Graph Distillation via Clustering cites this paper.

Simple yet Effective Graph Distillation via Clustering Graph Condensation for Graph Neural Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T13:55:20.916250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f9b4285-f6e4-44b5-a5e3-ba8c413a995b · inbound

Dynamic Graph Condensation cites this paper.

Dynamic Graph Condensation Graph Condensation for Graph Neural Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T00:46:08.921224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1df1273-4d6d-4a92-b607-3b309e350f38 · inbound

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms cites this paper.

Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms Graph Condensation for Graph Neural Networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T06:35:49.109883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6470d34f-f05b-4f23-9008-074e25c8ce81 · inbound

Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification cites this paper.

Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification Graph Condensation for Graph Neural Networks

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-14T00:43:31.962748Z

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 f50b142d-6fd7-484f-9ca3-c81b25ddc325 · inbound

Analytic Drift Resister for Non-Exemplar Continual Graph Learning cites this paper.

Analytic Drift Resister for Non-Exemplar Continual Graph Learning Graph Condensation for Graph Neural Networks

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:53:15.707715Z

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 eec7291c-fcb9-43bd-b4cd-efed569429d8 · inbound

An Efficient and Scalable Graph Condensation with Structure-Preserving cites this paper.

An Efficient and Scalable Graph Condensation with Structure-Preserving Graph Condensation for Graph Neural Networks

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T00:12:50.250679Z

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 60a0c8d0-b6f2-40e3-ad4f-8b504ca11935 · inbound

Geometry-Aware Dataset Condensation for Diffusion Model Training cites this paper.

Geometry-Aware Dataset Condensation for Diffusion Model Training Graph Condensation for Graph Neural Networks

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T12:26:56.814222Z

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.