Paper Citation Record · LEDGER

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs

As of 13 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 3 inbound Pith citation observations for arXiv:2412.00315.

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

pith.paper-citation-record.v1
2412.00315 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:35:47.457679Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:21:34.983036Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T04:52:17.415548Z

Reference resolution

42 of 42 outbound references displayed

verified exact1
verified fuzzy0
unresolved41
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1fb9cc1b-2f73-4dee-b5fd-bad67c6ac845 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.290342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b6cbeb4-7880-499b-9bfc-912768e1e72c · outbound

This paper cites LLaGA: Large Language and Graph Assistant.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs LLaGA: Large Language and Graph Assistant

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.299433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59a3bd87-53b1-4eaa-b78c-a3cc50a8cda6 · outbound

This paper cites Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.304560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20e4e27a-fd57-4ed0-898e-fddd2fd457b5 · outbound

This paper cites Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.308988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8b7dc3b-3069-4518-8345-81d505ee0774 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.313286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ae93b35-46a9-4f46-8e9b-ad4aeb27e302 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:35:48.015631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 90b98b18-bc15-48ab-bb2a-2be338a0a27f · outbound

This paper cites UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.324548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63a4f971-64ed-4bfb-b93e-57b7a8864604 · outbound

This paper cites GraphAlign: Pretraining One Graph Neural Network on Multiple Graphs via Feature Alignment.

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 b4ddf6b5-ef52-41b3-a842-8666b93bd53e · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.332605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab84b1f5-c497-4d5e-ad02-be502ab74505 · outbound

This paper cites PRODIGY: Enabling In-context Learning Over Graphs.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs PRODIGY: Enabling In-context Learning Over Graphs

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.337002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d4a0f72-c68f-4758-bff3-38208372b42c · outbound

This paper cites Automated Self-Supervised Learning for Graphs.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Automated Self-Supervised Learning for Graphs

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.341075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2abd960-4a97-434e-bab2-90ecad10bcbb · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:35:47.993077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bd6b61c4-65c9-464f-8fcd-501107f35321 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Adam: A Method for Stochastic Optimization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.350721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f261beca-b4a2-42c2-9cd4-a32df2604490 · outbound

This paper cites Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.354576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2f0eb5a-3e56-467b-93e2-816931b89be7 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.359354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b81024a7-508f-4eab-85d9-51d88783b2fe · outbound

This paper cites One for All: Towards Training One Graph Model for All Classification Tasks.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs One for All: Towards Training One Graph Model for All Classification Tasks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.363230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea244279-cc63-48b5-8aa8-b83eb1e1cd98 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.367281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f50421c2-8231-4c7b-bf1d-6879a5ed1abf · outbound

This paper cites Is Homophily a Necessity for Graph Neural Networks?.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Is Homophily a Necessity for Graph Neural Networks?

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.371307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7be4d9e3-70f9-461d-a360-a1cec6208600 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.375480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e896593-1829-4865-8876-406486287dcb · outbound

This paper cites Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.379447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85e59ba3-0dd1-4cec-8c6d-762fe5fad030 · outbound

This paper cites Position: Graph Foundation Models are Already Here.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Position: Graph Foundation Models are Already Here

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.383483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66e90fd7-4bd7-4caa-87dc-de438caccfdc · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:35:47.950589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e1aa5c79-98f6-4173-8a78-76784885c209 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.391148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24da74d8-8066-4a92-9934-fad327df02de · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.394423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc8f9295-91e8-4612-b7e1-ba8f8914d035 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.398464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32956843-8d74-44b1-a4ee-0c4ff4086ab2 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.402856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ffd76d1-e4d6-4d30-8674-3b38518eeef2 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:35:47.926973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b44b458d-2d96-4a8e-9a86-82d0c2b392bb · outbound

This paper cites GraphGPT: Graph Instruction Tuning for Large Language Models.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs GraphGPT: Graph Instruction Tuning for Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.410028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 207857e8-91f2-462a-8ca7-2988bc9bf268 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:35:47.914013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 60a69018-900e-428a-9986-82a9360da3b2 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.417183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 295c0103-e0cb-411d-a992-17cee786ffc8 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.420728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 721dd8bb-1be7-495b-a7b0-fa12acb93651 · outbound

This paper cites AnyGraph: Graph Foundation Model in the Wild.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs AnyGraph: Graph Foundation Model in the Wild

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.424356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82158f1b-b641-43f1-ad9c-6ecda3a3d611 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:35:47.887925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dfce1194-e216-4b5e-b04d-6947f0353b67 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.431868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 171e2c5a-2405-4ca8-8cf5-3ee73a208aee · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:35:47.862236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e8f26437-3632-4f34-b620-6814752df577 · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.439117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a157ae48-45a4-4aa3-aea8-ee5160a350da · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.446581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79c2d30f-6f57-4c73-b1fc-e7974468ebd9 · outbound

This paper cites Fully-inductive Node Classification on Arbitrary Graphs.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Fully-inductive Node Classification on Arbitrary Graphs

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.449932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb2a22f1-71af-47d6-b76b-153d6ae658af · outbound

This paper cites an unresolved cited work.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:35:47.832813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation af79cf46-361a-441f-9bb5-125629e3e669 · outbound

This paper cites Deep Graph Contrastive Representation Learning.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs Deep Graph Contrastive Representation Learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.457679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f771d3d9-b92f-45dc-b4cb-34a00c9226c1 · outbound

This paper cites In The world wide web conference.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs In The world wide web conference

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-12T05:35:47.316999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d421d84-2f25-473a-b5ee-9bd1b631663e · outbound

This paper cites LiGNN: Graph Neural Networks at LinkedIn.

One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs LiGNN: Graph Neural Networks at LinkedIn

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-12T05:35:47.817542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 604f74e1-b47e-4f2b-abad-2adec4281498 · inbound

H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs cites this paper.

H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:21:34.983036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 727b8a1b-55ec-40cf-9fb9-3f238d695904 · 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 One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:52:17.417448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0eb7747d-5b31-4d3e-b86a-01a336e28d3a · 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 One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T16:12:17.914741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.