Výsledky vyhledávání - "Graph Autoencoder"

Deep Learning on Graphs: A Survey Autor Zhang, Ziwei, Cui, Peng, Zhu, Wenwu

“…Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep…”
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Multi-view representation model based on graph autoencoder Autor Li, Jingci, Lu, Guangquan, Wu, Zhengtian, Ling, Fuqing

“…Graph representation learning is a hot topic in non-Euclidean data in various domains, such as social networks, biological networks, etc. When some data labels…”
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Assessing citation appropriateness through masked graph autoencoder Autor Avros, Renata, Volkovich, Zeev

“…This study proposes a novel network-based detection method for assessing citation reliability and identifying manipulative practices. Applying a Masked Graph…”
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A Comprehensive Survey on Graph Neural Networks Autor Wu, Zonghan, Pan, Shirui, Chen, Fengwen, Long, Guodong, Zhang, Chengqi, Yu, Philip S.

“…Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and…”
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Deep embedded clustering with distribution consistency preservation for attributed networks Autor Zheng, Yimei, Jia, Caiyan, Yu, Jian, Li, Xuanya

“…•A distribution consistency preserving deep embedded clustering model is proposed.•The model exploits GAE and AE to learn node representations and clusters…”
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Attribute imputation autoencoders for attribute-missing graphs Autor Xia, Riting, Zhang, Chunxu, Li, Anchen, Liu, Xueyan, Yang, Bo

“…Analyzing attribute-missing graphs with a complete topology, but missing the attributes of some nodes, is an emerging and challenging research topic. Data…”
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Learning Graph Embedding With Adversarial Training Methods Autor Pan, Shirui, Hu, Ruiqi, Fung, Sai-Fu, Long, Guodong, Jiang, Jing, Zhang, Chengqi

“…Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches…”
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Weighted dynamic network link prediction based on graph autoencoder Autor Mei, Peng, Zhao, Yuhong, Wang, Jingyu, Liang, Yefei

“…With the development of deep learning, Graph Autoencoders (GAE) within unsupervised learning frameworks have been widely applied to representation learning in…”
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NodeHGAE: Node-oriented heterogeneous graph autoencoder Autor Zhu, Xiangkai, Li, Chao, Yan, Yeyu, Zhao, Zhongying, Duan, Hua, Zeng, Qingtian

“…Heterogeneous graph autoencoder (HGAE), as an unsupervised learning approach, aims to encode nodes and edges of heterogeneous graphs into low-dimensional…”
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Graph-Based Bitcoin Fraud Detection Using Variational Graph Autoencoders and Supervised Learning Autor Koronaios, Argyrios, Koloniari, Georgia

“…Bitcoin is a decentralized cryptocurrency, which is rapidly growing and offering many advantages. Although its structure protects users from some types of…”
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Spatial-Spectral Graph Contrastive Clustering With Hard Sample Mining for Hyperspectral Images Autor Guan, Renxiang, Tu, Wenxuan, Li, Zihao, Yu, Hao, Hu, Dayu, Chen, Yuzeng, Tang, Chang, Yuan, Qiangqiang, Liu, Xinwang

“…Hyperspectral image (HSI) clustering is a fundamental yet challenging task that groups image pixels with similar features into distinct clusters. Among various…”
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Cross-domain recommendation via knowledge distillation Autor Li, Xiuze, Huang, Zhenhua, Wu, Zhengyang, Wang, Changdong, Chen, Yunwen

“…Recommendation systems frequently suffer from data sparsity, resulting in less-than-ideal recommendations. A prominent solution to this problem is Cross-Domain…”
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A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints Autor Yan, Xiongfeng, Yang, Min

“…Polyline and building simplification remain challenging in cartography. Most proposed algorithms are geometric-based and rely on specific rules. In this study,…”
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Enhancing effective connection with link prediction for session-based recommendation Autor Wang, Hui, You, Junjie, Yi, Jianbing, Liu, Junyan, Huang, Runjie

“…The core objective of session-based recommendation (SBR) is to precisely capture user interest preferences from massive candidate items by modeling short-term…”
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Clustering Diffusion Model with Frequency-Signal Modulation for Variational Graph Autoencoders Autor Cheng, Junwei, Liang, Ke, Feng, Pengxing, Liu, Weixiong, Tang, Yong, He, Chaobo

“…Variational autoencoders (VAEs) have been widely used for node clustering, with existing methods mainly focusing on enhancing the expressiveness of their…”
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Enhancing equipment safeguarding in IIoT: A self-supervised fault diagnosis paradigm based on asymmetric graph autoencoder Autor Chen, Zhuohang, Liu, Shen, Li, Chao, Chang, Yuanhong, Chen, Jinglong, Feng, Gaoshan, He, Shuilong

“…Thanks to the sufficient monitoring data provided by Industrial Internet of Things (IIoT), intelligent fault diagnosis technology has demonstrated remarkable…”
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RARE: Robust Masked Graph Autoencoder Autor Tu, Wenxuan, Liao, Qing, Zhou, Sihang, Peng, Xin, Ma, Chuan, Liu, Zhe, Liu, Xinwang, Cai, Zhiping, He, Kunlun

“…Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-training (SGP) paradigm due to its simplicity and effectiveness. However,…”
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Variational graph autoencoder-driven balancing strategy for multimodal multi-objective optimization Autor Yang, Lei, Zhang, Erlei, Dang, Qianlong

“…Multimodal multi-objective optimization aims to balance the diversity and the convergence to obtain multiple complete and uniform Pareto optimal solution sets…”
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TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting Autor Wu, Zonghan, Zheng, Da, Pan, Shirui, Gan, Quan, Long, Guodong, Karypis, George

“…This article aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for…”
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A Novel Group Recommendation Model With Two-Stage Deep Learning Autor Huang, Zhenhua, Liu, Yajun, Zhan, Choujun, Lin, Chen, Cai, Weiwei, Chen, Yunwen

“…Group recommendation has recently drawn a lot of attention to the recommender system community. Currently, several deep learning-based approaches are leveraged…”
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