Hugging Face Trending Papers July 13, 2026

Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

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Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Network and Large Language Model (LLM) based methods.

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arXiv Machine Learning

Jul 14

Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

arXiv:2607. 11374v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains.

By Chunyu Hu, Tianyin Liao, Ge Lan, Xingxuan Zhang, Jianxin Li, Peng Cui, Ziwei Zhang

llms diffusion benchmarks safety

arXiv Machine Learning

Sep 4

LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models

The paper introduces PromptGFM, a Graph Foundation Model designed for text‑attributed graphs (TAGs). It integrates Large Language Models (LLMs) and Graph Neural Networks (GNNs) through a Graph Understanding Module that prompts LLMs to emulate GNN workflows, and a Graph Inference Module that creates a language‑based graph vocabulary for better alignment and scalability. Experiments show PromptGFM outperforms existing methods and transfers effectively across various graphs and tasks.

By Xi Zhu, Haochen Xue, Ziwei Zhao, Wujiang Xu, Jingyuan Huang, Minghao Guo, Qifan Wang, Kaixiong Zhou, Imran Razzak, Yongfeng Zhang

llms fine-tuning safety

arXiv Machine Learning

Aug 21

GraphPFN: A Prior-Data Fitted Graph Foundation Model

arXiv:2509. 21489v4 Announce Type: replace Abstract: Graph foundation models face several fundamental challenges including transferability across diverse domains and data scarcity, which calls into question the very feasibility of creating such models.

By Dmitry Eremeev, Oleg Platonov, Gleb Bazhenov, Artem Babenko, Liudmila Prokhorenkova

llms fine-tuning benchmarks

arXiv Machine Learning

Aug 3

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models

arXiv:2607. 28980v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for enabling knowledge transfer across diverse domains.

By Yi Wang, Jitao Zhao, Di Jin, Dongxiao He

safety

arXiv AI

Jul 24

Semi-Supervised Text-Attributed Graph Distillation

arXiv:2607. 20477v1 Announce Type: new Abstract: {\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics.

By Yurui Lai, Samir Moustafa, Renchi Yang, Tsz Nam Chan

llms efficiency benchmarks

arXiv Machine Learning

4d ago

The Universal Classifier for Graph Learning

arXiv:2609.36302v1 Announce Type: new Abstract: While foundation models have revolutionized natural language processing and computer vision by leveraging universal vocabularies, Graph Machine Learnin...

By Ben Finkelshtein, Andr\'{e} Linhares, Petar Veli\v{c}kovi\'{c}, Bryan Perozzi, Mikhail Galkin

diffusion computer-vision nlp

One email a morning, machine-written

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Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models

GraphPFN: A Prior-Data Fitted Graph Foundation Model

Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models

Semi-Supervised Text-Attributed Graph Distillation

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