Paper Citation Record · LEDGER

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach

As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2601.21369.

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

pith.paper-citation-record.v1
2601.21369 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:16:58.871950Z

measured 19 of 19 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy0
unresolved18
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

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

This paper cites LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model.

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 bd8349b9-4371-4fe6-95eb-cb6d3caea2f8 · outbound

This paper cites Representing text for joint embedding of text and knowledge bases.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach Representing text for joint embedding of text and knowledge bases

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:57.871706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f8baac5-3a34-41e6-9d79-c9122287ee38 · outbound

This paper cites an unresolved cited work.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:57.944894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c89e3e0-4bb6-4b0c-a880-2a81433f6ad8 · outbound

This paper cites URL https://doi.org/ 10.1145/3394486.3403168.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach URL https://doi.org/ 10.1145/3394486.3403168

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:57.818978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b42cf1b1-27de-4093-8c87-50ad80b04552 · outbound

This paper cites ISBN 9798400714542.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach ISBN 9798400714542

Reference 11

Resolution: malformed identifier; no resolver link, observed 2026-08-04T06:16:58.068768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db726d96-48d2-445c-beed-eda081b14f9e · outbound

This paper cites Federated Graph Learning -- A Position Paper.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach Federated Graph Learning -- A Position Paper

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:58.212586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa17e08d-ab88-4b8d-9f3d-0f3a34db32ce · outbound

This paper cites To- wards effective federated graph foundation model via mitigating knowledge entanglement.arXiv preprint arXiv:2505.12684, 2025a.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach To- wards effective federated graph foundation model via mitigating knowledge entanglement.arXiv preprint arXiv:2505.12684, 2025a

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:58.409005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64ff99db-955d-45c0-b4ee-0e5f4707b8b8 · outbound

This paper cites Due to its emphasis on preserving structural integrity, GIN is frequently the preferred choice for graph-level representation tasks.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach Due to its emphasis on preserving structural integrity, GIN is frequently the preferred choice for graph-level representation tasks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:58.614732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd95af8f-74a7-49c1-a660-9555fd9fd629 · outbound

This paper cites The specific characteristics of these baselines are outlined below: FedAvg(McMahan et al.,.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach The specific characteristics of these baselines are outlined below: FedAvg(McMahan et al.,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:58.685018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27edc34b-a193-4965-b51d-aeb8939ac4bc · outbound

This paper cites an unresolved cited work.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach Unresolved cited work

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:58.758966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd4cb11a-7162-42f9-a880-ce30a877c12d · outbound

This paper cites an unresolved cited work.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:58.871950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba178a78-10ae-43ef-90e4-89925179a464 · outbound

This paper cites Regarding theFederated Adaptations of Centralized GFM Methods, andFedGFMswe strictly maintain the architectural backbones as reported in their respective original studies.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach Regarding theFederated Adaptations of Centralized GFM Methods, andFedGFMswe strictly maintain the architectural backbones as reported in their respective original studies

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:58.533837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e65ecd7-eaf7-46d7-ae16-2066456f4b4d · outbound

This paper cites Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:57.728507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27a54451-8149-4978-8a65-d71286b51eb9 · outbound

This paper cites FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:58.319019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ec50395-fe9d-4634-974f-42c8a730c294 · outbound

This paper cites ISBN 9781713829546.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach ISBN 9781713829546

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:58.134746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 379bbd5e-9288-44a0-9b55-775ada80e23d · outbound

This paper cites Rethinking Tokenized Graph Transformers for Node Classification.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach Rethinking Tokenized Graph Transformers for Node Classification

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:57.332244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3758ee9b-e7dc-4370-a262-b337c43bfdd1 · outbound

This paper cites AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:57.588340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07b70842-a62d-44a5-87be-b63920e5ca58 · outbound

This paper cites GOFA: A Generative One-For-All Model for Joint Graph Language Modeling.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach GOFA: A Generative One-For-All Model for Joint Graph Language Modeling

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:57.516579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4565bf95-a4ae-4c90-b596-88eb964b8d8b · outbound

This paper cites ISBN 9798400712456.

Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach ISBN 9798400712456

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T06:16:57.440278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.