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Akademický článek

Performance and mechanism of catalytic degradation of benzotriazole in water by Fe2O3-CuO/rCG

Autor: GUO Zirui, LI Hongyan, ZHANG Feng, CUI Jiali, YANG Qun, LI Yun

Publikováno v: Gongye shui chuli, Vol 44, Iss 9, Pp 118-126 (2024)

Iron-copper modified coal gangue(Fe2O3-CuO/rCG) was prepared using coal gangue(CG) as raw material through calcination, acid leaching and metal modification. The morphology and structure of Fe2O3-CuO/rCG were characterized and analyzed, and the degra

Externí odkaz: https://doaj.org/article/0e2670e63a8649bf87ed68f2b7b72c55

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LightRAG: Simple and Fast Retrieval-Augmented Generation

Autor: Guo, Zirui, Xia, Lianghao, Yu, Yanhua, Ao, Tu, Huang, Chao

Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs. However, existing RAG systems have signifi

Externí odkaz: http://arxiv.org/abs/2410.05779

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Intent-aware Recommendation via Disentangled Graph Contrastive Learning

Autor: Wang, Yuling, Wang, Xiao, Huang, Xiangzhou, Yu, Yanhua, Li, Haoyang, Zhang, Mengdi, Guo, Zirui, Wu, Wei

Publikováno v: [C]//Proceedings of the 32th international joint conference on artificial intelligence. 2023: 2343-2351

Graph neural network (GNN) based recommender systems have become one of the mainstream trends due to the powerful learning ability from user behavior data. Understanding the user intents from behavior data is the key to recommender systems, which pos

Externí odkaz: http://arxiv.org/abs/2403.03714

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GraphEdit: Large Language Models for Graph Structure Learning

Autor: Guo, Zirui, Xia, Lianghao, Yu, Yanhua, Wang, Yuling, Yang, Zixuan, Wei, Wei, Pang, Liang, Chua, Tat-Seng, Huang, Chao

Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural Networks (GNNs) have emerged as promising GSL solutions, utilizing rec

Externí odkaz: http://arxiv.org/abs/2402.15183

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Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction

Autor: Lu, Kangkang, Yu, Yanhua, Fei, Hao, Li, Xuan, Yang, Zixuan, Guo, Zirui, Liang, Meiyu, Yin, Mengran, Chua, Tat-Seng

In recent years, spectral graph neural networks, characterized by polynomial filters, have garnered increasing attention and have achieved remarkable performance in tasks such as node classification. These models typically assume that eigenvalues for

Externí odkaz: http://arxiv.org/abs/2401.15603

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