Paper Citation Record · LEDGER

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment

As of 10 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 3 inbound Pith citation observations for arXiv:2502.02017.

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

pith.paper-citation-record.v1
2502.02017 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:42:39.740689Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:12:20.234760Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T16:56:06.559446Z

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy4
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c6b1ec9b-56f1-47fc-a204-54aaf694608f · outbound

This paper cites Towards unsupervised deep graph structure learning.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Towards unsupervised deep graph structure learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T13:42:39.695012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df9576c6-62ad-431e-8485-65f76dbfcb7b · outbound

This paper cites Samgpt: Text-free graph foundation model for multi-domain pre- training and cross-domain adaptation.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Samgpt: Text-free graph foundation model for multi-domain pre- training and cross-domain adaptation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:42:39.943208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 703168e0-5119-4641-a30d-a7b30c1f8ae9 · outbound

This paper cites Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T13:42:39.715606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9b7adb4-89ff-4b38-b944-5a1e88fe21f4 · outbound

This paper cites A Survey on Graph Structure Learning: Progress and Opportunities.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment A Survey on Graph Structure Learning: Progress and Opportunities

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T13:42:39.725882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31ffbd4e-674a-4b56-b890-8161390fc98d · outbound

This paper cites an unresolved cited work.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-09T13:42:39.905296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 38ba3a5a-c26a-4861-86c9-379bda92598e · outbound

This paper cites an unresolved cited work.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-09T13:42:39.887686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 004a1f9f-d7f5-4a8f-877a-ff34de369bae · outbound

This paper cites Generalizing to unseen domains via distribution matching.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Generalizing to unseen domains via distribution matching

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-09T13:42:39.670233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b46c9af-5907-4b6c-9fed-ffad1e10130b · outbound

This paper cites The nodes are annotated with the reported gender of each user, and the objective is to predict this gender.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment The nodes are annotated with the reported gender of each user, and the objective is to predict this gender

Reference 2012

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:42:39.925531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 873ee0cb-576a-4154-8045-c770631f1735 · outbound

This paper cites A survey on in- context learning.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment A survey on in- context learning

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:42:40.009385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9f59ddfe-8db9-45f0-b395-696b3def08ef · outbound

This paper cites Self-supervised Learning on Graphs: Deep Insights and New Direction.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Self-supervised Learning on Graphs: Deep Insights and New Direction

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T13:42:39.689328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68610baa-89c9-4079-a5aa-14d819cd35ca · outbound

This paper cites Making Pre-trained Language Models Better Few-shot Learners.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Making Pre-trained Language Models Better Few-shot Learners

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T13:42:39.684082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e6eff70-f374-45fa-abb4-fcdb330827bb · outbound

This paper cites Inductive graph alignment prompt: Bridging the gap between graph pre- training and inductive fine-tuning from spectral perspec- tive.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Inductive graph alignment prompt: Bridging the gap between graph pre- training and inductive fine-tuning from spectral perspec- tive

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T13:42:39.959984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 12e5db13-3dd5-4d98-a176-802bff664c9a · outbound

This paper cites an unresolved cited work.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Unresolved cited work

Reference 2023

Resolution: unresolved; raw_fallback, observed 2026-08-09T13:42:39.980230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eb78cb77-54e7-4b6e-9c0a-7ea032ac471f · outbound

This paper cites Graph Neural Networks for Graphs with Heterophily: A Survey.

Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment Graph Neural Networks for Graphs with Heterophily: A Survey

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T13:42:39.720943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 57ba74e1-2000-4384-83e9-88aa1f686b39 · inbound

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding cites this paper.

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-02T23:38:55.221271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbb6522d-6a40-460a-b26e-57f4451ddb6f · inbound

Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment cites this paper.

Empowering Heterogeneous Graph Foundation Models via Decoupled Relation Alignment Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:56:06.561918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b1718545-f557-45a8-aecf-ee9b04a6c74e · inbound

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models cites this paper.

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T16:12:20.234760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.