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检索条件"主题词=Graph Learning"
846 条 记 录,以下是1-10 订阅
排序:
graph learning on Instruction Stream-Augmented CFG for Malware Variant Detection
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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 3015-3030页
作者: Mi, Jiaxin Li, Qi Han, Zewei Liao, Weilue Fu, Junsong Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China China Informat Technol Secur Evaluat Ctr Beijing 100085 Peoples R China
As malware as a service (MaaS) and organized attacks develop and drive a shift in malware variant generation mechanism, current variant detection, designed to counter conventional obfuscation and anti-detection strate... 详细信息
来源: 评论
graph learning With Riemannian Optimization for Multi-View Integrative Clustering
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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年 第1期9卷 381-393页
作者: Khan, Aparajita Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 India Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India
Real-world multi-view data may manifest as point-clouds, but their meaningful structure often resides on a lower dimensional manifold embedded in the higher dimensional space. Consequently, existing graph based multi-... 详细信息
来源: 评论
graph learning from incomplete graph signals: From batch to online methods
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SIGNAL PROCESSING 2025年 226卷
作者: Zhang, Xiang Wang, Qiao Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Sch Econ & Management Nanjing 210096 Peoples R China
Inferring graph topologies from data is crucial in many graph-related applications. Existing works typically assume that signals are observed at all nodes, which may not hold due to application-specific constraints. T... 详细信息
来源: 评论
graph learning Over Polytopic Uncertain graph
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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 716-720页
作者: Kishida, Masako Ono, Shunsuke Natl Inst Informat Principles Informat Res Div Tokyo 1018430 Japan Inst Sci Tokyo Dept Comp Sci Tokyo 1528550 Japan
This letter introduces a graph learning approach leveraging prior knowledge of graph topology. For this, we integrate the concept of polytopic uncertainty into existing approaches that learn graph Laplacians and adjac... 详细信息
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HHgraphSum: Hierarchical heterogeneous graph learning for extractive document summarization
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DISPLAYS 2025年 86卷
作者: Hao, Pengyi Wu, Cunqi Bai, Cong Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China
Extractive summarization aims to select important sentences from the document to generate a summary. However, current extractive document summarization methods fail to fully consider the semantic information among sen... 详细信息
来源: 评论
Multi-Level Resource-Coherented graph learning for Website Fingerprinting Attacks
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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 693-708页
作者: Gao, Bo Liu, Weiwei Liu, Guangjie Nie, Fengyuan Huang, Jianan Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Peoples R China Minist Educ Key Lab Intelligent Support Technol Complex Enviro Nanjing 210044 Peoples R China
Deep learning-based website fingerprinting (WF) attacks dominate website traffic classification. In the real world, the main challenges limiting their effectiveness are, on the one hand, the difficulty in countering t... 详细信息
来源: 评论
Cancer-drug response prediction via feature aggregation and association graph learning
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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 151卷
作者: Xu, Kaiyi Wang, Minhui Zou, Xin Ji, Chengfu Zhou, Hua Tang, Chang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Nanjing Med Univ Dept Pharm Lianshui Peoples Hosp Kangda Coll Huaian 223300 Peoples R China Funing Peoples Hosp Dept Hematol Yancheng 224400 Peoples R China
Predicting cancer-drug response (CDR) not only guides the development of anticancer drugs but also provides personalized treatment plans for cancer patients, driving the advancement of precision medicine. Currently, n... 详细信息
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Anomaly Detection and Localization via graph learning
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ENERGIES 2025年 第6期18卷 1475-1475页
作者: Amusan, Olabode Wu, Di North Dakota State Univ Dept Elect & Comp Engn Fargo ND 58105 USA
Phasor measurement units (PMUs) are being installed at an unprecedented rate on power systems, offering unique situation awareness capability. This paper presents a graph learning-based method for detecting and locati... 详细信息
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Distributed graph learning From Smooth Data: A Bayesian Framework
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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 1626-1642页
作者: Zhang, Jiayin Wu, Nan Zhang, Tingting Li, Bin Yan, Qinsiwei Ma, Xiaoli Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA
The emerging field of graph learning, which aims to learn reasonable graph structures from data, plays a vital role in graph Signal Processing (GSP) and finds applications in various data processing domains. However, ... 详细信息
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Diversity and consistency graph learning guided multi-view unsupervised feature selection
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KNOWLEDGE-BASED SYSTEMS 2025年 317卷
作者: Xu, Hanxiao Xu, Da Zhang, Yusen Shandong Univ Sch Math & Stat Weihai 264200 Shandong Peoples R China Liaocheng Univ Sch Math Sci Liaocheng 252000 Shandong Peoples R China
Multi-view unsupervised feature selection has gained considerable attention lately because of its potential to enhance the performance of clustering and classification tasks, as well as improve the efficiency and inte... 详细信息
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