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检索条件"主题词=Graph Autoencoder"
114 条 记 录,以下是111-120 订阅
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High-order autoencoder with data augmentation for collaborative filtering
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KNOWLEDGE-BASED SYSTEMS 2022年 240卷 107773-107773页
作者: Nguyen, Mo Yu, Jian Nguyen, Tung Yongchareon, Sira Auckland Univ Technol 2 Wakefield St Auckland Cbd New Zealand
Early DNN-based collaborative filtering (CF) approaches have demonstrated their superior performance than traditional CF such as Matrix Factorization. However, such approaches treat each user-item interaction as separ... 详细信息
来源: 评论
Mask-GVAE: Blind Denoising graphs via Partition  21
Mask-GVAE: Blind Denoising Graphs via Partition
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30th World Wide Web Conference (WWW)
作者: Li, Jia Liu, Mengzhou Zhang, Honglei Wang, Pengyun Wen, Yong Pan, Lujia Cheng, Hong Chinese Univ Hong Kong Hong Kong Peoples R China Tianjin Univ Tianjin Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China
We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs. We focus on recovering... 详细信息
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Learning graph Embedding With Adversarial Training Methods
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IEEE TRANSACTIONS ON CYBERNETICS 2020年 第6期50卷 2475-2487页
作者: Pan, Shirui Hu, Ruiqi Fung, Sai-fu Long, Guodong Jiang, Jing Zhang, Chengqi Monash Univ Fac Informat Technol Clayton Vic 3800 Australia Univ Technol Sydney Fac Engn & Informat Technol Ctr Artificial Intelligence Ultimo NSW 2007 Australia City Univ Hong Kong Dept Appl Social Sci Hong Kong Peoples R China
graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structur... 详细信息
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Cross-graph: Robust and Unsupervised Embedding for Attributed graphs with Corrupted Structure  20
Cross-Graph: Robust and Unsupervised Embedding for Attribute...
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20th IEEE International Conference on Data Mining (ICDM)
作者: Wang, Chun Han, Bo Pan, Shirui Jiang, Jing Niu, Gang Long, Guodong Univ Technol Sydney Australian Artificial Intelligence Inst Sydney NSW Australia Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Monash Univ Fac Informat Technol Clayton Vic Australia RIKEN Ctr Adv Intelligence Project Tokyo Japan
graph embedding has shown its effectiveness to represent graph information and capture deep relationships in graph data. Most recent graph embedding methods focus on attributed graphs, since they preserve both structu... 详细信息
来源: 评论