arXiv AI July 21, 2026 By Ridong Han, Yawen Shen, Zhongnian Li, Tongfeng Sun, Xinzheng Xu, Abdulmotaleb El Saddik

Selectivity Matters: Source Node Influence Pruning for Unsupervised Graph Domain Adaptation

arXiv:2607. 17668v1 Announce Type: cross Abstract: Unsupervised Graph Domain Adaptation (UGDA) aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph by mitigating cross-domain distribution shifts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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

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

Hugging Face Trending Papers

Jul 13

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

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.

llms diffusion benchmarks safety

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

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

Sep 15

Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learning

arXiv:2609.14045v1 Announce Type: new Abstract: Agentic learning systems are often required to adapt after deployment by observing new data and reusing prior knowledge under limited supervision or fe...

By Yingxu Wang, Kunyu Zhang, Siyang Gao

rag agents fine-tuning benchmarks

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Graph Domain Adaptation Does Not End with Representation Learning

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