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Paper Citation Record · LEDGER

Spiking Graph Convolutional Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2205.02767.

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

pith.paper-citation-record.v1
2205.02767 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:39:44.375837Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-24T08:46:05.573548Z

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 78a4fd89-bdb9-408c-a5c0-1714b9929ac7 · inbound

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation cites this paper.

SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation Spiking Graph Convolutional Networks

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-24T08:46:05.576777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-24T08:44:23.340180Z digest=sha256:427b728caf8d2bb3a9967fc0c864bace905480c4d9f3a2bf18b1457cb28c3d5c

Observation 876b4c2f-5a23-47d1-ae60-4c6632a5197d · inbound

Geometry-Aware Spiking Graph Neural Network cites this paper.

Geometry-Aware Spiking Graph Neural Network Spiking Graph Convolutional Networks

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-05T22:39:44.375837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:39:44.375837Z digest=sha256:a86159898ccd8568c704bb76fbe55c8f20063ac9ad97c174d36ce2eb1abe229a

Observation 1d5352c1-37c4-4216-b309-85748d333996 · inbound

TEFormer: Structured Bidirectional Temporal Enhancement Modeling in Spiking Transformers cites this paper.

TEFormer: Structured Bidirectional Temporal Enhancement Modeling in Spiking Transformers Spiking Graph Convolutional Networks

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-03T08:05:01.529512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T08:05:01.529512Z digest=sha256:c947b6e707cdbbaabe8eceb5c01b73616f07227036a097a9081e35a60d20d3a9

Observation 58ede34d-e876-4d24-a685-794a13dbc45d · inbound

Towards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-based Human Activity Recognition cites this paper.

Towards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-based Human Activity Recognition Spiking Graph Convolutional Networks

Reference 58

Resolution
malformed identifier
arxiv_id, observed 2026-05-11T09:00:58.516351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T16:22:14.115872Z digest=sha256:f74c49d1e0183c3afb98922054806fbe5138037a76fa123fed7a52e5256086d8

Observation 59c4f8e0-2018-4730-8150-73b0f9dda202 · inbound

Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs cites this paper.

Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs Spiking Graph Convolutional Networks

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-01T15:32:35.759119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T15:32:35.759119Z digest=sha256:12b87a9d010f1c27aab1472fad98cb4d343c24cd1d8ef03e1652ca0ea9b59b0a