arXiv Machine Learning

Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs

arXiv:2605. 04819v2 Announce Type: replace Abstract: Graph neural networks have been widely used in Boolean satisfiability (SAT) tasks to learn structural information from SAT formulas.

arXiv AI

Jul 10

MetaHGNIE: Meta-Path Induced Hypergraph Contrastive Learning in Heterogeneous Knowledge Graphs

arXiv:2512. 12477v2 Announce Type: replace Abstract: Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems.

By Jiawen Chen, Yanyan He, Qi Shao, Mengli Wei, Duxin Chen, Wenwu Yu, Yanlong Zhao

llms rag benchmarks safety

arXiv AI

Jul 17

DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs

arXiv:2512. 12477v3 Announce Type: replace Abstract: Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems.

By Jiawen Chen, Yanyan He, Qi Shao, Mengli Wei, Duxin Chen, Wenwu Yu, Yanlong Zhao

llms rag benchmarks safety

arXiv AI

Aug 6

NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning

arXiv:2608. 04381v1 Announce Type: cross Abstract: Self-supervised learning on graphs is largely shaped by contrastive methods that depend on carefully designed augmentations, and by generative methods that reconstruct node attributes in the input space.

By Tinghe Zhang, Jian Xu, Jiaheng Chen, Jiaxing Li, Yucheng Xiao, Qiang Wang

rag diffusion fine-tuning benchmarks

arXiv AI

1d ago

Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoning

arXiv:2608. 17443v1 Announce Type: new Abstract: Knowledge Graph Reasoning (KGR) aims to discover latent facts by leveraging the structural evidence available in KGs, posing a challenge to the structural semantic understanding capability of KGR models.

By Xingrui Zhuo, Jiapu Wang, Manzong Huang, Gongqing Wu, Xindong Wu

llms nlp fine-tuning benchmarks

arXiv Machine Learning

Jun 5

Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion

arXiv:2606. 05639v1 Announce Type: new Abstract: Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications.

By Dongxiao He, Ruqiong Zhang, Zhizhi Yu, Ling Ding, Di Jin, Guangquan Xu, Zhiyong Feng

llms benchmarks

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

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

Aug 11

CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization

arXiv:2603. 24304v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) deliver strong performance on graph tasks, but their accuracy drops significantly under out-of-distribution (OOD) scenarios.

By Bowen Lu, Liangqiang Yang, Teng Li, Kun Zhang

diffusion benchmarks

arXiv AI

Jun 2

Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning

arXiv:2508. 06588v3 Announce Type: replace-cross Abstract: Vector Quantization (VQ) has recently emerged as a promising approach for learning compressed and discrete representations for graph-structured data.

By Zian Zhai, Fan Li, Xingyu Tan, Xiaoyang Wang, Wenjie Zhang

efficiency benchmarks

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

Jun 11

Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data

arXiv:2606. 11946v1 Announce Type: cross Abstract: The conventional approach to deep learning over relational databases applies neural models, such as Graph Neural Networks (GNNs), to a graph representation of the database.

By Arie Soeteman, Balder ten Cate, Maurice Funk, Benny Kimelfeld, Carsten Lutz, Moritz Sch\"onherr

rag

arXiv Machine Learning

Aug 4

HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning

arXiv:2608. 00491v1 Announce Type: new Abstract: Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data.

By Ruichen Xu, Jingxiang Qu, Wenhan Gao, Jiaxing Zhang, Linsey Pang, Ravid Shwartz-Ziv, Yann LeCun, Yuefan Deng

rag diffusion benchmarks

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