Paper Citation Record · LEDGER

LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.14961.

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

pith.paper-citation-record.v1
2410.14961 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 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:04:14.191133Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T10:31:25.449903Z

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 b3428b61-e21d-4168-a2a4-ebcf997720f1 · inbound

Masked Language Models are Good Heterogeneous Graph Generalizers cites this paper.

Masked Language Models are Good Heterogeneous Graph Generalizers LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:14.191133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63b34b31-3263-46ca-b621-ea1a5bae1a01 · inbound

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models cites this paper.

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T23:37:56.282671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e819b708-56d3-4aa6-a0dc-a7d50480f7a7 · inbound

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach cites this paper.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:57.656361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0be46ab9-8dc3-4de4-9734-18a695b68b7c · inbound

GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning cites this paper.

GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:17.001122Z

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.

Observation 442f8bfc-e028-4153-925a-f40172c295f5 · inbound

Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models cites this paper.

Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-22T10:31:25.452829Z

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.

Observation fac6e4ab-4c32-4502-98bf-e80e90b2b286 · inbound

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models cites this paper.

Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model

Reference 100

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:22:54.406637Z

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.