arXiv Machine Learning

To Learn is to Wander: Learning Across Graphs and Tasks with Random Walks

arXiv AI

Jul 14

Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm

arXiv:2509. 24256v2 Announce Type: replace-cross Abstract: The pretrain-transfer paradigm, which underpins the success of large language models (LLMs), has demonstrated the immense power of creating foundation models that learn generalizable representations from vast datasets.

By Yunhao Liang, Pujun Zhang, Yuan Qu, Jingyuan Yang, Shaochong Lin, Zuo-jun Max Shen

llms

arXiv AI

Jun 17

Handling Feature Heterogeneity with Learnable Graph Patches

arXiv:2606. 17667v1 Announce Type: cross Abstract: In recent years, the rapid development of foundation models and graph pre-training technologies has spurred increasing interest in constructing a universal pre-trained graph model or Graph Foundation Model (GFM).

By Yifei Sun, Yang Yang, Xiao Feng, Zijun Wang, Haoyang Zhong, Chunping Wang, Lei Chen

rag

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

Sep 30

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

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

Sep 24

QUARTET: Quad-branch cross-Attention and Random-walk Traces for Enhancing Transformers on Relational Graphs

arXiv:2609.26855v1 Announce Type: cross Abstract: Relational Deep Learning (RDL) models multi-table databases as heterogeneous temporal graphs, and graph transformers currently achieve state-of-the-a...

By Kyaw Hpone Myint, Nan Jiang, Xiang Li, Zhe Wu, Alexandre G. R. Day, Pranab Mohanty, Giri Iyengar

llms benchmarks

arXiv Machine Learning

Jul 31

Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors

arXiv:2607. 28525v1 Announce Type: new Abstract: Many real-world graphs support multiple predictive tasks over the same underlying structure, creating an opportunity to reuse supervision across node classification (NC) and link prediction (LP).

By Neelam Akula, Surbhi Kumar, Murat Kantarcioglu, Baris Coskunuzer

arXiv Machine Learning

Jul 22

One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models

arXiv:2607. 19128v1 Announce Type: new Abstract: Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored.

By Jiayi Yang, Yifang Chen, Yuanfu Sun, Jiajin Liu, Qiaoyu Tan

llms rag multimodal benchmarks

Hugging Face Trending Papers

Sep 22

QUARTET: Quad-branch cross-Attention and Random-walk Traces for Enhancing Transformers on Relational Graphs

Relational Deep Learning (RDL) models multi-table databases as heterogeneous temporal graphs, and graph transformers currently achieve state-of-the-art performance on benchmarks like RelBench. However...

llms benchmarks

arXiv Machine Learning

Sep 23

Graph Domain Adaptation Does Not End with Representation Learning

arXiv:2609.25692v1 Announce Type: new Abstract: Graph domain adaptation (GDA) transfers knowledge from a labeled source graph to an unlabeled target graph under shifts in both node attributes and gra...

By Ziqian Liu, Yongxue Xu, Enze Zhang, Jiaqi Zhang, Hao Wang, Maolin Wang

fine-tuning benchmarks safety

arXiv Machine Learning

Sep 30

Message Passing Does More with Less for In-Context Learning on Graphs

arXiv:2609.37057v1 Announce Type: new Abstract: Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeat...

By Dooho Lee, Jinmo Lee, Minho Jeong, Kijung Shin, Jaemin Yoo

llms

arXiv AI

Sep 1

Call Neighbours Yourself: Graph Walks with Destination-Conditioned On-Policy Self-Distillation

arXiv:2608.29588v1 Announce Type: new Abstract: Reasoning over text-attributed graphs (TAGs) requires large language models (LLMs) to combine a node's text with evidence distributed across its neighb...

By Yilun Liu, Boyu Luo, Yanran Tang, Ruihong Qiu, Zi Huang

llms efficiency benchmarks

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