Results 1 to 10 of about 8,080,419 (296)
Multiscale Global Adaptive Attention Graph Neural Network [PDF]
Jisuanji kexue yu tansuo, 2023 Dynamic multiscale graph neural networks have high motion prediction errors due to the low correlation between the internal joints of body parts and the limited perceptual fields.
GOU Ruru, YANG Wenzhu, LUO Zifei, YUAN Yunfeng
doaj +3 more sources
MBHAN: Motif-Based Heterogeneous Graph Attention Network
Applied Sciences, 2022 Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and
Qian Hu +3 more
doaj +2 more sources
Disassemble Byte Sequence Using Graph Attention Network [PDF]
Journal of Universal Computer Science, 2022 Disassembly is the basis of static analysis of binary code and is used in malicious code detection, vulnerability mining, software optimization, etc. Disassembly of arbitrary suspicious code blocks (e.g., for suspicious traffic packets intercepted by the
Jing Qiu, Feng Dong, Guanglu Sun
doaj +4 more sources
Quantum-enhanced dual-layer graph attention network for time-series forecasting [PDF]
Scientific ReportsTime-series forecasting plays a pivotal role in domains such as traffic prediction, financial analysis, and energy consumption monitoring. However, real-world time series often exhibit intertwined patterns–trends, seasonality, and latent structures–that ...
Yongli Tang +4 more
doaj +2 more sources
Graph Attention Network Recommendation Method Based on Cross-Session Knowledge Graph [PDF]
Jisuanji gongcheng, 2023 The Session-Based Recommendation(SBR) method aims to predict the next item according to the sequence of anonymous user behaviors.However, most existing session recommendation methods typically model user preferences based on the current session, ignoring
ZHANG Xiaohui, MA Huifang, WANG Wentao, GAO Zihao
doaj +1 more source
User recommendation method integrating hierarchical graph attention network with multimodal knowledge graph [PDF]
Frontiers in NeuroroboticsIn common graph neural network (GNN), although incorporating social network information effectively utilizes interactions between users, it often overlooks the deeper semantic relationships between items and fails to integrate visual and textual feature ...
Xiaofei Han, Xiaofei Han, Xin Dou
doaj +2 more sources
Community Detection Fusing Graph Attention Network
Mathematics, 2022 It has become a tendency to use a combination of autoencoders and graph neural networks for attribute graph clustering to solve the community detection problem.
Ruiqiang Guo +4 more
doaj +1 more source
Rumor detection based on graph attention network [PDF]
ITM Web of Conferences, 2022 At present, most of the existing rumor detection methods focus on the learning and fusion of various features, but due to the complexity of language, these models often rarely consider the relationship between parts of speech.
Lv Yuwei +3 more
doaj +1 more source
Attention-Aware Heterogeneous Graph Neural Network
Big Data Mining and Analytics, 2021 As a powerful tool for elucidating the embedding representation of graph-structured data, Graph Neural Networks (GNNs), which are a series of powerful tools built on homogeneous networks, have been widely used in various data mining tasks.
Jintao Zhang, Quan Xu
doaj +1 more source
RAGAT: Relation Aware Graph Attention Network for Knowledge Graph Completion
IEEE Access, 2021 Knowledge graph completion (KGC) is the task of predicting missing links based on known triples for knowledge graphs. Several recent works suggest that Graph Neural Networks (GNN) that exploit graph structures achieve promising performance on KGC.
Xiyang Liu +3 more
doaj +1 more source