Paper Citation Record · LEDGER

One for All: Towards Training One Graph Model for All Classification Tasks

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:2310.00149.

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

pith.paper-citation-record.v1
2310.00149 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 28 of 28 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:55:23.669030Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:08:56.258128Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5601e2a7-2844-46bf-9e01-bda663249c7b · inbound

GOBench: Benchmarking Geometric Optics Generation and Understanding of MLLMs cites this paper.

GOBench: Benchmarking Geometric Optics Generation and Understanding of MLLMs One for All: Towards Training One Graph Model for All Classification Tasks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:23.669030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a058a99-bfd4-4e91-89ec-244273713cb2 · inbound

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

Masked Language Models are Good Heterogeneous Graph Generalizers One for All: Towards Training One Graph Model for All Classification Tasks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:14.300110Z

Source-reported events for the cited work

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Observation dd0c25b5-6daa-44e1-87ea-7fba408ee48a · inbound

Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment cites this paper.

Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment One for All: Towards Training One Graph Model for All Classification Tasks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T05:44:53.242558Z

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Observation ad94d738-870e-4d21-9b8b-c54b44345d04 · 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 for All: Towards Training One Graph Model for All Classification Tasks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:21:34.973120Z

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Observation 549d0ebe-17a6-4db1-a783-771c445e2e07 · inbound

Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark cites this paper.

Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark One for All: Towards Training One Graph Model for All Classification Tasks

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T00:55:54.768518Z

Source-reported events for the cited work

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Observation 7e4da553-463a-44eb-99d1-de1195529d05 · inbound

GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations cites this paper.

GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations One for All: Towards Training One Graph Model for All Classification Tasks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:40.606673Z

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Observation c6dbf751-68f6-4729-a142-ba296dee624d · inbound

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning cites this paper.

Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning One for All: Towards Training One Graph Model for All Classification Tasks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T22:42:24.882715Z

Source-reported events for the cited work

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Observation c0db666d-0e3c-4450-8925-73551a9f76d9 · inbound

Graph World Model cites this paper.

Graph World Model One for All: Towards Training One Graph Model for All Classification Tasks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:37:11.713264Z

Source-reported events for the cited work

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Observation 42fe4888-432c-43de-a2d8-1c6e730b61c8 · inbound

A Comprehensive Data-centric Overview of Federated Graph Learning cites this paper.

A Comprehensive Data-centric Overview of Federated Graph Learning One for All: Towards Training One Graph Model for All Classification Tasks

Reference 193

Resolution: unresolved; no resolver link, observed 2026-08-06T15:11:16.078316Z

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Observation e9c0a918-ce5c-4d5b-87ac-b462cd8191ce · inbound

GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning cites this paper.

GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning One for All: Towards Training One Graph Model for All Classification Tasks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T14:36:36.408242Z

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Observation fb698b0a-cccf-45f5-ade5-2bdf5e810787 · inbound

Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm cites this paper.

Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm One for All: Towards Training One Graph Model for All Classification Tasks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T14:42:34.054032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4119f2d0-c271-4800-9443-55b8e7126ad2 · inbound

GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning cites this paper.

GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning One for All: Towards Training One Graph Model for All Classification Tasks

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:27:31.606330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 21481ebf-a9bc-4c16-b730-2c3f197238f0 · inbound

Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs cites this paper.

Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs One for All: Towards Training One Graph Model for All Classification Tasks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T00:07:40.705713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4660d443-3e78-47c4-ae46-7bba309efa82 · 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 One for All: Towards Training One Graph Model for All Classification Tasks

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-22T10:31:25.514134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a8740c23-cacd-45e1-84f9-d438f6895079 · 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 for All: Towards Training One Graph Model for All Classification Tasks

Reference 122

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T04:52:17.406931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6c8ec294-b20d-4b3a-8ebe-87008b0c1f03 · inbound

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs cites this paper.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs One for All: Towards Training One Graph Model for All Classification Tasks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:53:17.643242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 85db4da7-f0b8-45d4-982d-3b1a63b4ca9b · inbound

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs cites this paper.

S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs One for All: Towards Training One Graph Model for All Classification Tasks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:59:50.870907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ddad0c45-faf9-4d3d-a23d-c3b25fa28f58 · inbound

G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs cites this paper.

G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs One for All: Towards Training One Graph Model for All Classification Tasks

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:26:17.559022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9b5dde4d-5d4b-48e6-866e-5b31dc00aa37 · inbound

Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation cites this paper.

Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation One for All: Towards Training One Graph Model for All Classification Tasks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-02T05:16:39.222272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca61133d-b760-41e8-b392-b17d69a52acf · inbound

A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation cites this paper.

A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation One for All: Towards Training One Graph Model for All Classification Tasks

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:46:26.718986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8fd29ed7-3384-4ece-bbff-5e7a224758a9 · inbound

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs cites this paper.

GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs One for All: Towards Training One Graph Model for All Classification Tasks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-03T08:57:47.494566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f1a77764-3b06-49d5-b3ea-1f558c8e8039 · inbound

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching cites this paper.

Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching One for All: Towards Training One Graph Model for All Classification Tasks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:07:47.681980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7cee52e5-483a-46cd-ac87-d411a3d597e3 · inbound

Handling Feature Heterogeneity with Learnable Graph Patches cites this paper.

Handling Feature Heterogeneity with Learnable Graph Patches One for All: Towards Training One Graph Model for All Classification Tasks

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:08:56.260274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 478838e7-e4ee-42a6-b82a-35f2bf87df8d · inbound

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks cites this paper.

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks One for All: Towards Training One Graph Model for All Classification Tasks

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:34:21.262611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 129bed47-7e79-40e5-9b46-1816ba84e903 · inbound

PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning cites this paper.

PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning One for All: Towards Training One Graph Model for All Classification Tasks

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-30T08:24:26.856671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 36a49855-3893-4454-ad04-7bd8d4f4c9a9 · inbound

UNIT: Unleash Large Language Models Potential for Graph Continual Learning cites this paper.

UNIT: Unleash Large Language Models Potential for Graph Continual Learning One for All: Towards Training One Graph Model for All Classification Tasks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-14T13:51:14.141383Z

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Observation c59ff686-bab1-41c0-8da5-3539228d178c · inbound

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation cites this paper.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation One for All: Towards Training One Graph Model for All Classification Tasks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T13:29:54.619115Z

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Unavailable: canonical work link unavailable.

Observation 5c741137-c652-4ae5-b428-d3227bd658da · 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 for All: Towards Training One Graph Model for All Classification Tasks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T16:12:17.694736Z

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Unavailable: canonical work link unavailable.