Results 1 to 10 of about 1,049,471 (309)

Utilizing correlation in space and time: Anomaly detection for Industrial Internet of Things (IIoT) via spatiotemporal gated graph attention network

Alexandria Engineering Journal
The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vital ...
Yuxin Fan, Tingting Fu, Nikolai Izmailovich Listopad, Peng Liu, Sahil Garg, Mohammad Mehedi Hassan   +5 more
doaj   +1 more source

ST-GRAT: A Novel Spatio-temporal Graph Attention Networks for Accurately Forecasting Dynamically Changing Road Speed

International Conference on Information and Knowledge Management, 2020
Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over ...
Cheonbok Park, Chunggi Lee, Hyojin Bahng, Yunwon Tae, Seungmin Jin, Kihwan Kim, Sungahn Ko, J. Choo   +7 more
semanticscholar   +1 more source

Attention-driven Graph Clustering Network

Proceedings of the 29th ACM International Conference on Multimedia, 2021
The combination of the traditional convolutional network (i.e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional network captures the topological graph feature.
Peng, Zhihao, Liu, Hui, Jia, Yuheng, Hou, Junhui   +3 more
openaire   +2 more sources

Adaptive Propagation Graph Convolutional Networks Based on Attention Mechanism

Information, 2022
The main steps in a graph neural network are message propagation and aggregation between nodes. Message propagation allows messages from distant nodes in the graph to be transmitted to the central node, while feature aggregation allows the central node ...
Chenfang Zhang, Yong Gan, Ruisen Yang
doaj   +1 more source

Contextual Recommendations: Dynamic Graph Attention Networks With Edge Adaptation

IEEE Access
Recommender systems have witnessed a great shift in leveraging contextual information as an auxiliary resource to improve the quality of the recommendations.
Driss El Alaoui, J. Riffi, Abdelouahed Sabri, Badraddine Aghoutane, Ali Yahyaouy, H. Tairi   +5 more
semanticscholar   +1 more source

Enhancing Graph Summarization Using Node Importance and Graph Attention Networks

Mathematics
As the scale of graph-structured data continues to grow, graph summarization has become an important technique for storage efficiency and high-level visualization.
Krista Rizman Žalik, Domen Mongus, Mitja Žalik   +2 more
doaj   +1 more source

Dynamic graph attention networks for point cloud landslide segmentation

International Journal of Applied Earth Observations and Geoinformation, 2023
Accurate landslide segmentation is crucial for obtaining damage information in disaster mitigation and relief efforts. This study aims to develop a deep learning network for accurate point cloud landslide segmentation.
Ruilong Wei, Chengming Ye, Yonggang Ge, Yao Li, Jonathan Li   +4 more
doaj   +1 more source

DeepInf: Social Influence Prediction with Deep Learning

, 2018
Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them.
Duchi John, Hamilton Will, Kipf Thomas N, Niepert Mathias, Ulyanov Dmitry, Vaswani Ashish, Velickovic Petar   +6 more
core   +1 more source

Biomedical Word Sense Disambiguation Based on Graph Attention Networks

IEEE Access, 2022
Biomedical words have many semantics. Biomedical word sense disambiguation (WSD) is an important research issue in biomedicine field. Biomedical WSD refers to the process of determining meanings of ambiguous word according to its context.
Chun-Xiang Zhang, Ming-Lei Wang, Xue-Yao Gao   +2 more
doaj   +1 more source

Session-based Recommendation with Graph Neural Networks

, 2019
The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations.
Tan, Tieniu, Tang, Yuyuan, Wang, Liang, Wu, Shu, Xie, Xing, Zhu, Yanqiao   +5 more
core   +1 more source