arXiv AI July 8, 2026 By Zifan Wang, Siyu Chen, Wenzhuo Song

Signed-Graph Recommendation as Structural Consistency Maximization

arXiv:2607. 05952v1 Announce Type: cross Abstract: While signed social recommendation has shown great potential by modeling both trust and distrust relations, its effectiveness is often hindered by structural noise and data sparsity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

efficiency

arXiv AI

Jun 16

Guiding Federated Graph Recommendation with LLM-encoded knowledge

arXiv:2606. 15277v1 Announce Type: cross Abstract: Graph-based recommender systems are highly effective at extracting collaborative signals from user--item interactions, and federated learning (FL) allows these models to be trained while preserving user privacy.

By Thi Minh Chau Nguyen, Hien Trang Nguyen, Duc Anh Nguyen, Van Ho-Long, Thanh Trung Huynh, Zhao Ren

llms rag benchmarks safety

arXiv Machine Learning

Jun 16

TCHG: Tri-Trust Conditioned Heterogeneous Graph Learning for Reliable Dynamic Trust Prediction

arXiv:2606. 16611v1 Announce Type: new Abstract: Trust prediction infers latent user-user trust relations and provides important support for social recommendation, fake-review and manipulation detection, and risk identification.

By Bohao Liao, Boyu Deng, Qipeng Song, Jieling Wang, Jingchao Wang

robotics

arXiv AI

Jun 9

GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation

arXiv:2606. 07526v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong potential for recommendation (LLMRec) due to their powerful reasoning and generalization abilities.

By Lin Mu, Guoji Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

llms rag benchmarks

arXiv AI

Jun 16

Model Graph Inductive Learning for Knowledge Graph Completion

arXiv:2606. 16509v1 Announce Type: new Abstract: Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations.

By Mohommad Esmaei Khani, Mahdieh Hasheminejad, Ali Taherkhani, Hossein Hajiabolhassan

rag benchmarks

arXiv Machine Learning

Jun 3

Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings

arXiv:2606. 03365v1 Announce Type: new Abstract: Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs.

By Guillaume M\'erou\'e, Fabien Gandon, Pierre Monnin

rag benchmarks

arXiv AI

Jul 29

TRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation

arXiv:2607. 25471v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) and Large Language Models (LLMs) have each advanced recommendation systems by modeling structural and semantic signals, respectively.

By He Ma, Chen Liu

llms rag benchmarks

Related stories

Guiding Federated Graph Recommendation with LLM-encoded knowledge

TCHG: Tri-Trust Conditioned Heterogeneous Graph Learning for Reliable Dynamic Trust Prediction

GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation

Model Graph Inductive Learning for Knowledge Graph Completion

Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings

TRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation