Results 61 to 70 of about 1,903,201 (339)

Bilinear Graph Neural Network with Neighbor Interactions

, 2020
Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the ...
Feng, Fuli, He, Xiangnan, Li, Yan, Wang, Xiang, Zhang, Yongdong, Zheng, Kai, Zhu, Hongmin   +6 more
core   +1 more source

Network Slicing End-to-end Latency Prediction Based on Heterogeneous Graph Neural Network [PDF]

Jisuanji kexue
End-to-end latency,as a crucial performance metric for network slicing,is difficult to predict accurately via modeling due to the influences of network topology,traffic model,and scheduling policies.To tackle the above issues,we propose a heterogeneous ...
HU Haifeng, ZHU Yiwen, ZHAO Haitao
doaj   +1 more source

scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses

Nature Communications, 2021
Single-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex ...
Juexin Wang, A. Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Ren Qi, Cankun Wang, H. Fu, Qin Ma, Dong Xu   +9 more
semanticscholar   +1 more source

Graph Clustering with Graph Neural Networks

, 2020
Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction. However, important unsupervised problems on graphs, such as graph clustering, have proved more resistant to advances in GNNs.
Tsitsulin, Anton, Palowitch, John, Perozzi, Bryan, Müller, Emmanuel   +3 more
openaire   +3 more sources

Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network

Sensors, 2023
In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time–frequency images as inputs to convolutional neural networks ...
Dong Wang, Meiyan Lin, Xiaoxu Zhang, Yonghui Huang, Yan Zhu   +4 more
doaj   +1 more source

Locally Private Graph Neural Networks [PDF]

Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021
Graph Neural Networks (GNNs) have demonstrated superior performance in learning node representations for various graph inference tasks. However, learning over graph data can raise privacy concerns when nodes represent people or human-related variables that involve sensitive or personal information.
Sajadmanesh, Sina, Gatica-Perez, Daniel
openaire   +2 more sources

Two-Level Graph Neural Network [PDF]

IEEE Transactions on Neural Networks and Learning Systems
Graph Neural Networks (GNNs) are recently proposed neural network structures for the processing of graph-structured data. Due to their employed neighbor aggregation strategy, existing GNNs focus on capturing node-level information and neglect high-level information.
Xing Ai, Chengyu Sun, Zhihong Zhang, Edwin R. Hancock   +3 more
openaire   +5 more sources

Modality Fusion Vision Transformer for Hyperspectral and LiDAR Data Collaborative Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, collaborative classification of multimodal data, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR), has been widely used to improve remote sensing image classification accuracy.
Bin Yang, Xuan Wang, Ying Xing, Chen Cheng, Weiwei Jiang, Quanlong Feng   +5 more
doaj   +1 more source

Spectrum-based deep neural networks for fraud detection

, 2017
In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as
Li, Jun, Lu, Aidong, Wu, Xintao, Yuan, Shuhan   +3 more
core   +1 more source

Motif Graph Neural Network

IEEE Transactions on Neural Networks and Learning Systems
Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations.
Xuexin Chen, Ruichu Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao   +5 more
openaire   +4 more sources