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Paper Citation Record · LEDGER

A simple yet effective baseline for non-attributed graph classification

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1811.03508.

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

pith.paper-citation-record.v1
1811.03508 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T21:04:19.395944Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T06:06:41.464296Z

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 b5186ab7-58b5-4361-9931-d6246d4b1b49 · inbound

Fast Graph Representation Learning with PyTorch Geometric cites this paper.

Fast Graph Representation Learning with PyTorch Geometric A simple yet effective baseline for non-attributed graph classification

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:01:23.684741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-13T20:01:23.649737Z digest=sha256:900de25a7af9f51d8fd3b07179a2348323624adaa825cb7f2a8d663815511cc0

Observation 8ad7d776-db9c-4312-b908-e3848e2d208a · inbound

Enhancing the Utility of Higher-Order Information in Relational Learning cites this paper.

Enhancing the Utility of Higher-Order Information in Relational Learning A simple yet effective baseline for non-attributed graph classification

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-07T21:04:19.395944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T21:04:19.395944Z digest=sha256:3b6172fc8b67881a340759ef51917ad5d2f44a8864af49c4c64e1b4c24e7b54c

Observation 13727764-0883-44b5-9cf8-9ef139f37eca · inbound

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning cites this paper.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning A simple yet effective baseline for non-attributed graph classification

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T15:43:40.385675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:43:40.385675Z digest=sha256:9b4fea763b2218042539dcafe92efd21dd74b7e280da1963c66b13ff6a82b18c

Observation fff4d1a7-04f0-46d2-b100-e13ef1d1ea78 · inbound

Graph Contrastive Learning versus Untrained Baselines: The Role of Dataset Size cites this paper.

Graph Contrastive Learning versus Untrained Baselines: The Role of Dataset Size A simple yet effective baseline for non-attributed graph classification

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-05T12:31:08.904576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:31:08.904576Z digest=sha256:597c07a5978c869064e196569a51fe5cf671d5c22a2a67c861473bc5c578b990

Observation c6dd5088-51a4-4d07-aff6-014322c3afd1 · inbound

Learning to accelerate distributed ADMM using graph neural networks cites this paper.

Learning to accelerate distributed ADMM using graph neural networks A simple yet effective baseline for non-attributed graph classification

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-18T18:46:45.083248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T18:46:36.084583Z digest=sha256:cd5c53dee7f37b4dbdabc435d76e2f82d71d9a57823bff97a99904f5f99b5713

Observation 1d966bf7-f60e-4c3e-bcb9-3ab5a1ee985c · inbound

Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes cites this paper.

Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes A simple yet effective baseline for non-attributed graph classification

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:31:15.971687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-07T16:51:24.821614Z digest=sha256:8b578414014522c7ef8ef61a2aee6f876c6d30baf506c17d6a6c8eb72f5ac8b8

Observation 3d70888e-2dd6-468d-962b-bdadaa567729 · inbound

Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning cites this paper.

Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning A simple yet effective baseline for non-attributed graph classification

Reference 145

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T06:06:41.465604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-28T07:37:20.073677Z digest=sha256:6493fff45b8c45c7f27e2c9eec4a9de51e92018e506c9cd38cc56272b4364346

Observation 0a8e2d80-9a2e-48ed-9e68-4b832126ebfe · inbound

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls cites this paper.

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls A simple yet effective baseline for non-attributed graph classification

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-01T07:41:30.826937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T07:41:30.826937Z digest=sha256:d8feb0204ad93f11dba41e9b04f518bcbe3b66d20fe9661c78873d3f08ec5945