15 papers · ranked by Valyu relevance
Si Zhang, Hanghang Tong, Jiejun Xu, Ross Maciejewski
Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very…
Xiao-Meng Zhang, Li Liang, Lin Liu, Ming-Jing Tang
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of…
Bin Wang, LvHang Cheng, JinFang Sheng, ZhengAng Hou + 1 more
With the advent of the wave of big data, the generation of more and more graph data brings great pressure to the traditional deep learning model. The birth of graph neural network fill the gap of deep learning in graph data. At present, graph convolutional networks (GCN) have surpassed traditional methods such as…
Pan Yang, Xinxin Zhang, Marc Brecht
Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC). However, limited training data and spectral uncertainty restrict the classification performance, and the computational demands of a graph convolution network (GCN) present challenges for…
Bin Wang, Bodong Cai, Jinfang Sheng, Wenzhe Jiao
In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power. Graph neural network methods within deep learning have shown remarkable capabilities in processing graph-structured data, such as social networks and traffic…
Fangyuan Lei, Xun Liu, Qingyun Dai, Bingo Wing-Kuen Ling + 2 more
'Huimin Zhao' 'Yan Liu'] With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity.…
Lizhen Shao, Cong Fu, Xunying Chen
Background Autism spectrum disorder (ASD) is a serious developmental disorder of the brain. Recently, various deep learning methods based on functional magnetic resonance imaging (fMRI) data have been developed for the classification of ASD. Among them, graph neural networks, which generalize deep neural network models…
Yuchen Zhou, Hongtao Huo, Zhiwen Hou, Fanliang Bu + 1 more
Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to…
Zhao Chen, Danilo Comminiello, Tokunbo Ogunfunmi, Nithin V. George
Non-Euclidean data, such as social networks and citation relationships between documents, have node and structural information. The Graph Convolutional Network (GCN) can automatically learn node features and association information between nodes. The core ideology of the Graph Convolutional Network is to aggregate node…
Xiaoxiao Zhang, Chenyun Dai, Yao Guo, Bogdan Ovidiu Popescu
The electroencephalogram (EEG), widely used for measuring the brain’s electrophysiological activity, has been extensively applied in the automatic detection of epileptic seizures. However, several challenges remain unaddressed in prior studies on automated seizure detection: (1) Methods based on CNN and LSTM assume…
Van Thuy Hoang, Hyeon-Ju Jeon, Eun-Soon You, Yoewon Yoon + 3 more
Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. Graph representation learning aims to map graph entities to low-dimensional…
Qiaoyu Ma, Zongying Lai, Zi Wang, Yiran Qiu + 2 more
Background Recent Convolutional Neural Networks (CNNs) perform low-error reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image with kernels and successfully explore the local information. Nonetheless, the non-local image information, which is embedded among image patches relatively…
Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed, Masahiro Takatsuka
'Masahiro Takatsuka'] Title: Graphical abstract
Maham Saeidi, Waldemar Karwowski, Farzad V. Farahani, Krzysztof Fiok + 5 more
'P. A. Hancock' 'Ben D. Sawyer' 'Leonardo Christov-Moore' 'Pamela K. Douglas' 'Valerio Santangelo'] Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific experimental paradigms. Deep learning models have increasingly been applied for decoding and encoding purposes study…
Bin Wu, Mei Xue, Ying Jia, Ning Zhang + 4 more
'Chunlei Zhang' 'Guangyin Jin'] Human Activity Recognition (HAR) plays a pivotal role in video understanding, with applications ranging from surveillance to virtual reality. Skeletal data has emerged as a robust modality for HAR, overcoming challenges such as noisy backgrounds and lighting variations. However, current…