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检索条件"主题词=Graph Contrastive Learning"
302 条 记 录,以下是31-40 订阅
排序:
Node importance evaluation in heterogeneous network based on attention mechanism and graph contrastive learning
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NEUROCOMPUTING 2025年 626卷
作者: Shu, Jian Zou, Yiling Cui, Hui Liu, Linlan Nanchang Hangkong Univ Sch Software Nanchang 330063 Peoples R China Nanchang Hangkong Univ Sch Informat Engn Nanchang 330063 Peoples R China
Recently, heterogeneous networks have attracted widespread attention as a modeling approach for complex networks. However, the complex structure and diversity of semantic relations of heterogeneous networks pose chall... 详细信息
来源: 评论
Interdependence-Adaptive Mutual Information Maximization for graph contrastive learning
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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年 第12期36卷 8556-8567页
作者: Sun, Qingqiang Wang, Kai Zhang, Wenjie Cheng, Peng Lin, Xuemin Great Bay Univ Sch Engn Dongguan 523000 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia East China Normal Univ Sch Software Engn Shanghai 200062 Peoples R China Shanghai Jiao Tong Univ Univ Antai Coll Econ & Management Shanghai 200030 Peoples R China
Despite remarkable advancements in graph contrastive learning techniques, the identification of interdependent relationships when maximizing cross-view mutual information remains a challenging issue, primarily due to ... 详细信息
来源: 评论
Seeking False Hard Negatives for graph contrastive learning
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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年 第8期34卷 7454-7466页
作者: Liu, Xin Qian, Biao Liu, Haipeng Guo, Dan Wang, Yang Wang, Meng Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230601 Peoples R China
graph contrastive learning (GCL) has achieved great success in self-supervised representation learning throughout positive and negative pairs based on graph neural networks (GNNs), where one critical issue lies in how... 详细信息
来源: 评论
Breaking the curse of dimensional collapse in graph contrastive learning: A whitening perspective
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INFORMATION SCIENCES 2024年 657卷
作者: Tao, Yang Guo, Kai Zheng, Yizhen Pan, Shirui Cao, Xiaofeng Chang, Yi Jilin Univ Sch Artificial Intelligence Changchun 130012 Jilin Province Peoples R China Monash Univ Dept Data Sci & AI Melbourne Vic Australia Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4222 Australia
Dimensional collapse in graph contrastive learning (GCL) confines node embeddings to their lower-dimensional subspace, diminishing their distinguishability. However, the causes and solutions of this curse remain relat... 详细信息
来源: 评论
An Efficient and Lightweight Spectral-Spatial Feature graph contrastive learning Framework for Hyperspectral Image Clustering
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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷
作者: Yang, Aitao Li, Min Ding, Yao Xiao, Xiongwu He, Yujie Xian Res Inst High Technol Xian 710025 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China
Due to the scarcity of prior information and the high complexity of spectral data, hyperspectral image (HSI) clustering presents a significant challenge. Although recent deep clustering methods have demonstrated remar... 详细信息
来源: 评论
Multi-Network graph contrastive learning for Cancer Driver Gene Identification
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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年 第4期11卷 3430-3440页
作者: Peng, Wei Zhou, Zhengnan Dai, Wei Yu, Ning Wang, Jianxin Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650093 Peoples R China Kunming Univ Sci & Technol Comp Technol Applicat Key Lab Yunnan Prov Kunming 650093 Peoples R China State Univ New York Dept Comp Sci Coll Brockport Brockport NY 14422 USA Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South Univ Hunan Prov Key Lab Bioinformat Changsha 410083 Peoples R China
Identifying driver genes contributing to the occurrence and development of cancers plays a critical role in cancer research and treatment. Some recent computational approaches identify cancer-driver genes based on gen... 详细信息
来源: 评论
Federal graph contrastive learning With Secure Cross-Device Validation
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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年 第12期23卷 14145-14158页
作者: Wang, Tingqi Zheng, Xu Zhang, Jinchuan Tian, Ling Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China
Distributed mobile devices collect unlabeled graph data from environment. Introducing popular graph contrastive learning (GCL) methods can learn node representations better. However, training high-performance GCL requ... 详细信息
来源: 评论
Multi-aspect graph contrastive learning for Review-enhanced Recommendation
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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2024年 第2期42卷 1-29页
作者: Wang, Ke Zhu, Yanmin Zang, Tianzi Wang, Chunyang Liu, Kuan Ma, Peibo Shanghai Jiao Tong Univ Dept Comp Sci & Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China
Review-based recommender systems explore semantic aspects of users' preferences by incorporating user-generated reviews into rating-based models. Recent works have demonstrated the potential of review information ... 详细信息
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Adaptive graph contrastive learning with joint optimization of data augmentation and graph encoder
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KNOWLEDGE AND INFORMATION SYSTEMS 2024年 第3期66卷 1657-1681页
作者: Wu, Zhenpeng Chen, Jiamin Al-Sabri, Raeed Oloulade, Babatounde Moctard Gao, Jianliang Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China
graph contrastive learning (GCL) has been successfully used to solve the problem of the huge cost of graph data annotation, such as labor cost, time cost, and professional knowledge cost. Recent works have focused on ... 详细信息
来源: 评论
Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual graph contrastive learning
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ADVANCED SCIENCE 2024年 第3期12卷 e2410081页
作者: Yu, Zhuohan Yang, Yuning Chen, Xingjian Wong, Ka-Chun Zhang, Zhaolei Zhao, Yuming Li, Xiangtao Jilin Univ Sch Artificial Intelligence Jilin 130012 Peoples R China Univ Toronto Terrence Donnelly Ctr Cellular & Biomol Res Toronto ON M5S 3E1 Canada Massachusetts Gen Hosp Cutaneous Biol Res Ctr Harvard Med Sch Boston MA 02115 USA City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China Northeast Forestry Univ Coll Comp & Control Engn Harbin 150040 Peoples R China
Recent advances in spatial transcriptomics have enabled simultaneous preservation of high-throughput gene expression profiles and the spatial context, enabling high-resolution exploration of distinct regional characte... 详细信息
来源: 评论