Results 71 to 80 of about 6,341,676 (193)

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.
Zhihao Peng 0002, Hui Liu 0032, Yuheng Jia, Junhui Hou   +3 more
openaire   +2 more sources

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

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

Deep Graph Attention Networks

2024 Twelfth International Symposium on Computing and Networking Workshops (CANDARW)
Graphs are useful for representing various realworld objects. However, graph neural networks (GNNs) tend to suffer from over-smoothing, where the representations of nodes of different classes become similar as the number of layers increases, leading to performance degradation.
Jun Kato 0008, Airi Mita, Keita Gobara, Akihiro Inokuchi   +3 more
openaire   +4 more sources

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

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

A Review on Graph Theory in Deep Learning

NTU Journal of Pure Sciences
Graph theory has gained popularity as a flexible tool in machine learning to capture complex correlations between objects in non-Euclidean structured data.
Ayhan Ahmed Al-Shumam, Arjuwan M. A. Al-Jawadi   +1 more
doaj   +1 more source

Symmetry in complex networks

, 2008
We consider the size and structure of the automorphism groups of a variety of empirical ‘real-world’ networks and find that, in contrast to classical random graph models, many real-world networks are richly symmetric.
Sanchez-Garcia, Ruben J., Anderson, James W., MacArthur, Ben D.   +2 more
core   +1 more source

Sparse graphs-based dynamic attention networks

Heliyon
In previous research, the prevailing assumption was that Graph Neural Networks (GNNs) precisely depicted the interconnections among nodes within the graph's architecture. Nonetheless, real-world graph datasets are often rife with noise, elements that can
Runze Chen, Kaibiao Lin, Binsheng Hong, Shandan Zhang, Fan Yang   +4 more
doaj   +1 more source

Rail Transit Prediction Based on Multi-View Graph Attention Networks

Journal of Advanced Transportation, 2022
Traffic prediction is the cornerstone of intelligent transportation system. In recent years, graph neural network has become the mainstream traffic prediction method due to its excellent processing ability of unstructured data.
Li Wang, Xin Wang, Jiao Wang
doaj   +1 more source