Suchergebnisse - "Graph masked autoencoder"

Rethinking link prediction: A multi-scale graph masked autoencoder von Zhang, Guotai, Zuo, Enguang, Yan, Ziwei, Chen, Chen, Chen, Cheng, Lv, Xiaoyi

“… Therefore, we revisit these two approaches from a novel perspective and propose a multi-scale graph masked autoencoder (MS-GMAE …”
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DVGMAE: Self-Supervised Dynamic Variational Graph Masked Autoencoder von Gao, Mengzhou, Zhang, Xinxun, Jiao, Pengfei, Li, Tianpeng, Zhao, Zhidong

“… Although contrastive self-supervised learning (SSL) on dynamic graphs has made significant success, the issue of heavy reliance on data augmentation and …”
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Learning-Induced Channel Extrapolation for Fluid Antenna Systems Using Asymmetric Graph Masked Autoencoder von Zhang, Haibin, Wang, Jiale, Wang, Chao, Wang, Cheng-Cai, Wong, Kai-Kit, Wang, Bin, Chae, Chan-Byoung

“… In so doing, we then contrive a customized solution, referred to as an asymmetric graph masked autoencoder (AGMAE …”
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MAEST: accurately spatial domain detection in spatial transcriptomics with graph masked autoencoder von Zhu, Pengfei, Shu, Han, Wang, Yongtian, Wang, Xiaofeng, Zhao, Yuan, Hu, Jialu, Peng, Jiajie, Shang, Xuequn, Tian, Zhen, Chen, Jing, Wang, Tao

“… MAEST leverages graph masked autoencoders to denoise and refine representations while incorporating graph contrastive learning to prevent feature collapse and enhance model robustness …”
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GMAEEG: A Self-Supervised Graph Masked Autoencoder for EEG Representation Learning von Fu, Zanhao, Zhu, Huaiyu, Zhao, Yisheng, Huan, Ruohong, Zhang, Yi, Chen, Shuohui, Pan, Yun

“… To alleviate these challenges, this work proposes a self-supervised graph masked autoencoder for EEG representation learning, named GMAEEG …”
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Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis von Li, Yueying, Zhang, Xiaotong, Guan, Shihan, Ma, Guolin, Kong, Youyong

“… -individual associations in population. To solve these problems, this work proposes a novel approach for detecting abnormal neural circuits associated with brain diseases, named Topology-guided Graph Masked autoencoder Learning method (TGML …”
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KGMAEDDI: Knowledge Graph and Molecular-Graph Masked Autoencoder for Drug-Drug Interaction Prediction von Li, Yu, You, Zhu-Hong, Yang, Yuan, Mi, Cheng-gang

“… Drug-drug interaction (DDI) prediction is essential for drug development and clinical safety. Early studies mainly relied on large labeled datasets and focused …”
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DMSDRec: Dynamic Structure-Aware Graph Masked Autoencoder and Spatiotemporal Diffusion for Next-POI Recommendation von Li, Yue, Zeng, Jun, Tang, Haoran, Wen, Junhao, Gao, Min, Zhou, Wei

“… ). Specifically, we introduce a dynamic structure-aware improved graph masked autoencoder that adaptively and dynamically distills global transitional …”
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STGMAE: A GNSS data-driven pre-training spatiotemporal graph masked autoencoder for agricultural machinery trajectory operation mode identification von Chen, Tailai, Zhai, Weixin

“… Utilizing spatiotemporal features in massive amounts of trajectory data to identify the operation mode of agricultural machinery trajectories is a key task in …”
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Joint masking and self-supervised strategies for inferring small molecule-miRNA associations von Zhou, Zhecheng, Zhuo, Linlin, Fu, Xiangzheng, Lv, Juan, Zou, Quan, Qi, Ren

“… Inferring small molecule-miRNA associations (MMAs) is crucial for revealing the intricacies of biological processes and disease mechanisms. Deep learning, …”
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Graph Masked Autoencoder for Sequential Recommendation von Ye, Yaowen, Xia, Lianghao, Huang, Chao

“… In light of this, we propose a simple yet effective Graph Masked AutoEncoder-enhanced sequential Recommender …”
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IGedgeMAE: Topology-Aware Graph Masked Autoencoder with Dynamic Edge Importance Guidance von Zhang, Guotai, Xie, Xia, Sun, Ruishuang, Zuo, Enguang

“… In the context of the digital integration of culture and tourism in Beiting, most of the existing related data mining technical methods only focus on the …”
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UMGAD: Unsupervised Multiplex Graph Anomaly Detection von Li, Xiang, Qi, Jianpeng, Zhao, Zhongying, Zheng, Guanjie, Cao, Lei, Dong, Junyu, Yu, Yanwei

“… Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary objective of identifying anomalous nodes that deviate …”
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Graph Masked Autoencoder for Spatio-Temporal Graph Learning von Zhang, Qianru, Wang, Haixin, Siu-Ming Yiu, Yin, Hongzhi

“… To address these challenges, we propose a novel spatio-temporal graph masked autoencoder paradigm that explores generative self-supervised learning for effective spatio-temporal data augmentation …”
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GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction von Shi, Yucheng, Dong, Yushun, Tan, Qiaoyu, Li, Jundong, Liu, Ninghao

“… To tackle this issue, we propose a novel graph masked autoencoder framework called GiGaMAE …”
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Multilevel Contrastive Graph Masked Autoencoders for Unsupervised Graph-Structure Learning von Fu, Sichao, Peng, Qinmu, He, Yang, Wang, Xiaorui, Zou, Bin, Xu, Duanquan, Jing, Xiao-Yuan, You, Xinge

“… performance in different graph analytical tasks, how to utilize the popular graph masked autoencoder to sufficiently acquire effective …”
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Self-Supervised Graph Masked Autoencoders for Hyperspectral Image Classification von Hu, Zhenghao, Tu, Bing, Liu, Bo, He, Yan, Li, Jun, Plaza, Antonio

“… To counter these problems, this work investigates a feature extraction module based on self-supervised graph masked autoencoders (SGMAEs …”
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MGM-AE: Self-Supervised Learning on 3D Shape Using Mesh Graph Masked Autoencoders von Yang, Zhangsihao, Ding, Kaize, Liu, Huan, Wang, Yalin

“… Our method, Mesh Graph Masked Autoencoders (MGM-AE), utilizes masked autoencoding to pre-train the model and extract important features from the data …”
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Heterogeneous Graph Masked Autoencoders von Tian, Yijun, Dong, Kaiwen, Zhang, Chunhui, Zhang, Chuxu, Chawla, Nitesh V

“… ? In light of this, we study the problem of generative SSL on heterogeneous graphs and propose HGMAE, a novel heterogeneous graph masked autoencoder model to address these challenges …”
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CMGAE: Enhancing Graph Masked Autoencoders through the Use of Contrastive Learning von Yang, Weiwu, Zhou, Liang

“… Contrastive learning and generative methodologies in graph self-supervised learning offer efficient strategies for managing graph data with scarce labels …”
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