Hugging Face Trending Papers June 10, 2026

Probabilistic Salary Prediction with Graph Attention Networks and a Mixture Density Network

Read the original on Hugging Face Trending Papers →

Accurate salary prediction is critical for bridging the information gap between employers and job seekers in modern labor markets. Existing approaches predominantly yield a single point estimate and treat job attributes such as location, occupation, and industry as independent categorical features, ignoring both the inherent uncertainty and multi-modality of real-world compensation data and the rich hierarchical and semantic-similarity relationships that govern pay norms.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

llms

arXiv Machine Learning

Jun 11

Probabilistic Salary Prediction with Graph Attention Networks and a Mixture Density Network

arXiv:2606. 11663v1 Announce Type: cross Abstract: Accurate salary prediction is critical for bridging the information gap between employers and job seekers in modern labor markets.

By Zhipei Qin, Mohammad Shokri, N. van Weeren, F. W. Takes

llms

arXiv Machine Learning

Jun 30

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks

arXiv:2606. 29773v1 Announce Type: new Abstract: Graphs are widely used to model relational systems, with applications in domains such as social networks, finance, and biomedicine.

By Haoxin Sun, Yiqing Lin, Yajun Huang, Chenhui Dong, Mingjun Li, Zhongzhi Zhang

llms diffusion 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

arXiv AI

Aug 10

Harnessing the Synergy between LLM Agents and Knowledge Graphs for Urban Socioeconomic Prediction

arXiv:2411. 00028v3 Announce Type: replace-cross Abstract: Socioeconomic prediction aims to leverage various urban data to predict the socioeconomic indicators of regions such as population and commercial activity level, which plays an important role in understanding urban regions and supporting decision-making.

By Zhilun Zhou, Jingyang Fan, Yu Liu, Fengli Xu, Depeng Jin, Yong Li

llms rag agents

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

Hugging Face Trending Papers

Jun 29

PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning

Text-Attributed Graphs (TAGs) combine textual semantics with graph structure and are central to many graph learning tasks. However, existing fusion methods often treat text and structure as separate inputs in a shallow, one-way pipeline, which limits deep interaction between modalities and weakens performance under sparse connectivity or cross-graph generalisation.

llms multimodal safety

Related stories

Probabilistic Salary Prediction with Graph Attention Networks and a Mixture Density Network

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks

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

Harnessing the Synergy between LLM Agents and Knowledge Graphs for Urban Socioeconomic Prediction

Handling Feature Heterogeneity with Learnable Graph Patches

PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning