Paper Citation Record · LEDGER

GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.02953.

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

pith.paper-citation-record.v1
2406.02953 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 2026-08-13T06:32:02.005865+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:35:47.328790Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T04:52:17.359984Z

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 63a4f971-64ed-4bfb-b93e-57b7a8864604 · inbound

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs cites this paper.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.328790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7586267c-62d2-436a-9767-be8ff63651f2 · inbound

UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs cites this paper.

UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T17:44:03.841506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db8baef9-dbe5-4a2f-8e20-b6ab6ba898f0 · inbound

BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions cites this paper.

BioMol-MQA: A Multi-Modal Question Answering Dataset For LLM Reasoning Over Bio-Molecular Interactions GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:13.912430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79ac0448-d93a-4092-be28-ef2a31122928 · inbound

Unified Graph Prompt Learning via Low-Rank Graph Message Prompting cites this paper.

Unified Graph Prompt Learning via Low-Rank Graph Message Prompting GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment

Reference 44

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T09:16:02.786496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6dc07024-d314-43dc-902a-0eea517f0338 · inbound

SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory cites this paper.

SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:52:17.361523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b3890d85-47a6-40d3-99d4-e02bb63b77dd · inbound

What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models cites this paper.

What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-31T21:59:21.806411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.