GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning | Paper Citation Record

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This paper cites Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Unresolved cited work

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This paper cites Bronstein, and Max Hansmire.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Bronstein, and Max Hansmire

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This paper cites Llaga: Large language and graph assistant.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Llaga: Large language and graph assistant

Reference 4

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This paper cites Exploring the potential of large language models (llms)in learning on graphs.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Exploring the potential of large language models (llms)in learning on graphs

Reference 5

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This paper cites Graph Machine Learning in the Era of Large Language Models (LLMs).

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Graph Machine Learning in the Era of Large Language Models (LLMs)

Reference 6

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This paper cites Universal prompt tuning for graph neural networks.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Universal prompt tuning for graph neural networks

Reference 7

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This paper cites Fast Graph Representation Learning with PyTorch Geometric.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Fast Graph Representation Learning with PyTorch Geometric

Reference 8

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This paper cites Hamilton, Zhitao Ying, and Jure Leskovec.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Hamilton, Zhitao Ying, and Jure Leskovec

Reference 9

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This paper cites Harnessing explanations: LLM -to- LM interpreter for enhanced text-attributed graph representation learning.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Harnessing explanations: LLM -to- LM interpreter for enhanced text-attributed graph representation learning

Reference 10

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This paper cites TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

Reference 13

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This paper cites u ller, Lennart Purucker, Arjun Krishnakumar, Max K \.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning u ller, Lennart Purucker, Arjun Krishnakumar, Max K \

Reference 14

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This paper cites Open graph benchmark: Datasets for machine learning on graphs.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Open graph benchmark: Datasets for machine learning on graphs

Reference 15

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This paper cites Pande, and Jure Leskovec.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Pande, and Jure Leskovec

Reference 16

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This paper cites Let's ask GNN: empowering large language model for graph in-context learning.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Let's ask GNN: empowering large language model for graph in-context learning

Reference 17

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This paper cites Prodigy: enabling in-context learning over graphs.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Prodigy: enabling in-context learning over graphs

Reference 18

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This paper cites Variational Graph Auto-Encoders.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Variational Graph Auto-Encoders

Reference 19

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This paper cites Kipf and Max Welling.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Kipf and Max Welling

Reference 20

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This paper cites GOFA: A generative one-for-all model for joint graph language modeling.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning GOFA: A generative one-for-all model for joint graph language modeling

Reference 21

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This paper cites Unified graph neural networks pre-training for multi-domain graphs.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Unified graph neural networks pre-training for multi-domain graphs

Reference 24

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This paper cites One for all: Towards training one graph model for all classification tasks.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning One for all: Towards training one graph model for all classification tasks

Reference 25

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This paper cites Yu, and Chuan Shi.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Yu, and Chuan Shi

Reference 26

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This paper cites Graphprompt: Unifying pre-training and downstream tasks for graph neural networks.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Graphprompt: Unifying pre-training and downstream tasks for graph neural networks

Reference 27

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This paper cites In-context time series predictor.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning In-context time series predictor

Reference 28

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Position: Graph foundation models are already here

Reference 29

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

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

Reference 30

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This paper cites Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala

Reference 31

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This paper cites Graph language models.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Graph language models

Reference 32

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This paper cites Learning transferable visual models from natural language supervision.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Learning transferable visual models from natural language supervision

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This paper cites Chawla, and Chao Huang.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Chawla, and Chao Huang

Reference 34

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Unresolved cited work

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Multi-scale attributed node embedding

Reference 36

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Pitfalls of Graph Neural Network Evaluation

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Unresolved cited work

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning GPPT: graph pre-training and prompt tuning to generalize graph neural networks

Reference 39

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This paper cites All in one: Multi-task prompting for graph neural networks.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning All in one: Multi-task prompting for graph neural networks

Reference 40

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Graph Prompt Learning: A Comprehensive Survey and Beyond

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

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Graphicl: Unlocking graph learning potential in llms through structured prompt design

Reference 42

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Graphgpt: Graph instruction tuning for large language models

Reference 43

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This paper cites Llms as zero-shot graph learners: Alignment of GNN representations with LLM token embeddings.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Llms as zero-shot graph learners: Alignment of GNN representations with LLM token embeddings

Reference 44

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This paper cites Model Generalization on Text Attribute Graphs: Principles with Large Language Models.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Model Generalization on Text Attribute Graphs: Principles with Large Language Models

Reference 45

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Unresolved cited work

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This paper cites Images speak in images: A generalist painter for in-context visual learning.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Images speak in images: A generalist painter for in-context visual learning

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Gft: Graph foundation model with transferable tree vocabulary

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning MoleculeNet: A Benchmark for Molecular Machine Learning

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Opengraph: Towards open graph foundation models

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This paper cites How powerful are graph neural networks? In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning How powerful are graph neural networks? In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Bhowmick, and Juncheng Liu

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Cohen, and Ruslan Salakhutdinov

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This paper cites Contextual structure knowledge transfer for graph neural networks.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Contextual structure knowledge transfer for graph neural networks

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Prasanna

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Graphtranslator: Aligning graph model to large language model for open-ended tasks

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This paper cites All in one and one for all: A simple yet effective method towards cross-domain graph pretraining.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning All in one and one for all: A simple yet effective method towards cross-domain graph pretraining

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This paper cites Bronstein, and Jian Tang.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Bronstein, and Jian Tang

Reference 60

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Fug: Feature-universal graph contrastive pre-training for graphs with diverse node features

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning RELIEF: reinforcement learning empowered graph feature prompt tuning

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GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Graphclip: Enhancing transferability in graph foundation models for text-attributed graphs

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This paper cites Prog: A graph prompt learning benchmark.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Prog: A graph prompt learning benchmark

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T11:30:53.803683Z

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Observation 6e9a5e1b-c5bd-4689-882c-4a2c085298e9 · outbound

This paper cites write newline.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning write newline

Reference 65

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Observation 65147e98-a7f7-474f-9ad8-24c30d1df73a · outbound

This paper cites @esa (Ref.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning @esa (Ref

Reference 66

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Observation 5be1b5cf-6f7e-4a41-be1c-2f8218209def · outbound

This paper cites an unresolved cited work.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Unresolved cited work

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-04T11:30:54.025779Z

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Observation 27d9c2b2-4b95-4039-a921-745fedf9cc99 · outbound

This paper cites an unresolved cited work.

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning Unresolved cited work

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-04T11:30:54.097963Z

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