Paper Citation Record · LEDGER

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling

As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.03799.

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

pith.paper-citation-record.v1
2505.03799 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:31:05.831009Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

47 of 47 outbound references displayed

verified exact2
verified fuzzy8
unresolved37
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 054bc2d4-258a-470e-bf30-cf8f8136be70 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 1

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Source-reported events for the cited work

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Observation 78d05784-ba7a-4db2-89fb-cc4109c71737 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling LLaMA: Open and Efficient Foundation Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.564981Z

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Observation fba1df1f-444b-469f-a5f6-c0fa2a7baaa8 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Learning Transferable Visual Models From Natural Language Supervision

Reference 3

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Source-reported events for the cited work

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Observation 2cde9c55-99e2-45d0-b8d7-eb5c018d153d · outbound

This paper cites Flamingo: a Visual Language Model for Few-Shot Learning.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Flamingo: a Visual Language Model for Few-Shot Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.576328Z

Source-reported events for the cited work

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Observation bcb9e8c3-0399-4aa9-8431-e787b3f5fcb4 · outbound

This paper cites BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

Reference 5

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Source-reported events for the cited work

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Observation 7e340581-3f2d-413b-a926-d780c297ee7d · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.586742Z

Source-reported events for the cited work

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Observation 830567b4-d968-4476-822e-98f866a023bc · outbound

This paper cites Graph-Bert: Only Attention is Needed for Learning Graph Representations.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Graph-Bert: Only Attention is Needed for Learning Graph Representations

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.592494Z

Source-reported events for the cited work

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Observation 572b90da-296c-4803-b30e-28232284330d · outbound

This paper cites Graphicl: Unlocking graph learning potential in llms through structured prompt design,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Graphicl: Unlocking graph learning potential in llms through structured prompt design,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:31:06.804276Z

Source-reported events for the cited work

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Observation 488dbe08-9e12-4cfb-a196-e0a73ea731d1 · outbound

This paper cites LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.618702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a088644a-2659-43ab-8fac-cedb107761ad · outbound

This paper cites Can we soft prompt llms for graph learning tasks?.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Can we soft prompt llms for graph learning tasks?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.623718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c81cf562-a620-45e1-a04a-c94f5fa8ad31 · outbound

This paper cites A survey of graph meets large language model: Progress and future directions,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling A survey of graph meets large language model: Progress and future directions,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:31:06.786309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e61b0a34-fd51-4c06-a7fb-a8fa00502efa · outbound

This paper cites Challenges and opportunities in gnn-llm integration,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Challenges and opportunities in gnn-llm integration,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:31:06.769606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ae43084d-2447-48e8-9263-1066ad71f669 · outbound

This paper cites LLaGA: Large Language and Graph Assistant.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling LLaGA: Large Language and Graph Assistant

Reference 15

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Source-reported events for the cited work

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Observation 28d62395-b304-4b34-b25f-82711cd19f61 · outbound

This paper cites A Note on Over-Smoothing for Graph Neural Networks,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling A Note on Over-Smoothing for Graph Neural Networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:31:06.752482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0b99e848-16ac-4820-9a8a-c42ff1472ecf · outbound

This paper cites Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 17

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Source-reported events for the cited work

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Observation cb7b0195-2420-4e21-88f9-68e8e1a1dcd4 · outbound

This paper cites Language is All a Graph Needs.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Language is All a Graph Needs

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.664494Z

Source-reported events for the cited work

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Observation 01edd159-7ecd-4561-ac36-6e8070c9c5b5 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Semi-Supervised Classification with Graph Convolutional Networks

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26dcbff7-21c7-45bf-97a9-fc2fe507ff02 · outbound

This paper cites Graph Attention Networks.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Graph Attention Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.677254Z

Source-reported events for the cited work

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Observation b6e91533-1ebf-4880-9e57-444881d6846c · outbound

This paper cites Inductive Representation Learning on Large Graphs.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Inductive Representation Learning on Large Graphs

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.685486Z

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Observation 02b7a2cc-27d9-4a3a-8c03-94bce7f6e240 · outbound

This paper cites GPT-4 Technical Report.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling GPT-4 Technical Report

Reference 22

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Observation b7179ccd-c334-4d60-a08e-1e4d76e61429 · outbound

This paper cites Scaling instruction-finetuned language models,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Scaling instruction-finetuned language models,

Reference 23

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Observation 5782b229-7cea-441e-a48a-50b6665fbcd9 · outbound

This paper cites GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.703500Z

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Observation c3587d7a-c41c-4825-aba7-f9e16901c17e · outbound

This paper cites A survey of large language models for graphs,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling A survey of large language models for graphs,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:31:06.725209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5fd6a5d6-3083-4cee-99e5-c7f1e2d779ea · outbound

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

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling GraphGPT: Graph Instruction Tuning for Large Language Models

Reference 26

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Observation 8ce2ff35-03d7-40e1-80e0-0e7e085f27d6 · outbound

This paper cites HiGPT: Heterogeneous Graph Language Model.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling HiGPT: Heterogeneous Graph Language Model

Reference 27

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Observation cb4cc75f-8648-407c-b7f9-1fffb447c2cc · outbound

This paper cites Graphllm: Boosting graph reasoning ability of large language model,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Graphllm: Boosting graph reasoning ability of large language model,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.727277Z

Source-reported events for the cited work

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Observation fdab10ca-4215-4d38-9bcc-3a1f1550cfcd · outbound

This paper cites Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.739167Z

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Observation c81755ca-b3d7-4ce4-8ae2-f8804b0c4c62 · outbound

This paper cites OpenGraph: Towards Open Graph Foundation Models.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling OpenGraph: Towards Open Graph Foundation Models

Reference 30

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Observation 0b7435a0-7250-4592-b96c-68f5d055aa79 · outbound

This paper cites GreaseLM: Graph REASoning Enhanced Language Models for Question Answering.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling GreaseLM: Graph REASoning Enhanced Language Models for Question Answering

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.749881Z

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Observation 1e271753-f18e-46d8-830b-092a2a3652d9 · outbound

This paper cites Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs

Reference 32

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Observation ef87397f-5bea-4623-be5d-9fad8262b060 · outbound

This paper cites Walklm: A uniform language model fine-tuning framework for attributed graph embedding,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Walklm: A uniform language model fine-tuning framework for attributed graph embedding,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:31:06.697832Z

Source-reported events for the cited work

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Observation ae37f767-d403-43d0-ab1a-809c4669d8d8 · outbound

This paper cites Enhancing graph representation learning with walklm for effective community detection,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Enhancing graph representation learning with walklm for effective community detection,

Reference 34

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Observation 8aa304fe-0336-4876-8d50-e40ec58c6bdc · outbound

This paper cites GraphWiz: An Instruction-Following Language Model for Graph Problems.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling GraphWiz: An Instruction-Following Language Model for Graph Problems

Reference 35

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Observation 20b4a409-b37e-4a35-8c29-338e7c42012d · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling A Generalization of Transformer Networks to Graphs

Reference 36

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Observation dd71df03-7ee3-4d9e-bd02-7859fd6d3ba3 · outbound

This paper cites Do transformers really perform bad for graph representation?.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Do transformers really perform bad for graph representation?

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:31:06.820706Z

Source-reported events for the cited work

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Observation 60e4afab-aa32-4da4-a07f-9a8050a795bd · outbound

This paper cites Graph convolutional neural networks for web-scale recommender systems,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Graph convolutional neural networks for web-scale recommender systems,

Reference 38

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Observation c01ae631-c6dc-4d75-8f3b-28dcf2a792df · outbound

This paper cites Revisiting Semi-Supervised Learning with Graph Embeddings.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Revisiting Semi-Supervised Learning with Graph Embeddings

Reference 39

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Source-reported events for the cited work

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Observation 27593ac7-ef8e-48d7-a80a-587e5e1e0c85 · outbound

This paper cites Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.805308Z

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Observation c3ea1c45-bae6-4b4b-a3e5-898e3186c2d3 · outbound

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Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Open graph benchmark: Datasets for machine learning on graphs,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:31:06.681169Z

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Observation afc69ea7-3444-4e5b-8372-f4726a2c1b26 · outbound

This paper cites Do Transformers Really Perform Bad for Graph Representation?.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Do Transformers Really Perform Bad for Graph Representation?

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.787509Z

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Observation 7aa752e1-66e2-4869-b382-5219c5ca99b3 · outbound

This paper cites When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:31:05.898545Z

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Observation 4eaa510f-c63c-4204-80b5-d9bfb7502783 · outbound

This paper cites Pubmed text similarity model and its application to curation efforts in the conserved domain database,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Pubmed text similarity model and its application to curation efforts in the conserved domain database,

Reference 44

Resolution: verified exact; doi, observed 2026-08-16T04:31:05.872862Z

Source-reported events for the cited work

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Observation 3ad6b90e-8401-414a-918b-1b75f0e16f93 · outbound

This paper cites Decoupled weight decay regularization,.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Decoupled weight decay regularization,

Reference 47

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Source-reported events for the cited work

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Observation ffe41436-ea32-467b-8d74-abe846e63a02 · outbound

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Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling Decoupled Weight Decay Regularization

Reference 2019

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Observation bf24838c-e50b-4828-9409-c9dc7e0ec21e · outbound

This paper cites GraphLLM: Boosting Graph Reasoning Ability of Large Language Model.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.732843Z

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Observation 32f01aa3-7cc2-4ae0-ab5d-acab7009194d · outbound

This paper cites A Survey of Graph Meets Large Language Model: Progress and Future Directions.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling A Survey of Graph Meets Large Language Model: Progress and Future Directions

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.633287Z

Source-reported events for the cited work

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Observation 40cf9b60-da90-4364-a051-eb56d4fbaf63 · outbound

This paper cites GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design.

Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:05.613232Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.