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20232026
most citedGenerative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning

2 citations · 2 across the 10 of their papers we have counts for

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5 papers · 1 filter

cs.AI2025

DevPiolt: Operation Recommendation for IoT Devices at Xiaomi Home

Yuxiang Wang, Siwen Wang, Haowei Han +10

Operation recommendation for IoT devices refers to generating personalized device operations for users based on their context, such as historical operations, environment informatio…

cs.CL2025

How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAG

Qiming Zeng, Hao Luo, Yuhao Lin +5

By retrieving contexts from knowledge graphs, graph-based retrieval-augmented generation (GraphRAG) enhances large language models (LLMs) to generate quality answers for user quest…

cs.AI2025

Guiding LLM-based Smart Contract Generation with Finite State Machine

Hao Luo, Yuhao Lin, Xiao Yan +5

Smart contract is a kind of self-executing code based on blockchain technology with a wide range of application scenarios, but the traditional generation method relies on manual co…

cs.CL2025

Exploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph

Yuxiang Wang, Xiao Yan, Shiyu Jin +6

Text-attributed graph (TAG) provides a text description for each graph node, and few- and zero-shot node classification on TAGs have many applications in fields such as academia an…

cs.DC2025

MTGenRec: An Efficient Distributed Training System for Generative Recommendation Models in Meituan

Yuxiang Wang, Chi Ma, Xiao Yan +15

Recommendation is crucial for both user experience and company revenue in Meituan as a leading lifestyle company, and generative recommendation models (GRMs) are shown to produce q…