Graph Neural Networks (GNNs) have been widely used for graph learning tasks. The main aspect of GNN's layer-wise message passing is conducting attribute/feature propagation on graph. Most existing GNNs generally conduct feature propagation across all feature dimensions. However, in many real ...
In this paper, a sparse graph neural network-aided (SGNN-aided) decoder is proposed for improving the decoding performance of polar codes under bursty interference. Firstly, a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding. To further ...
LGNN: a novel linear graph neural network algorithm The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification perfor... S Cao,X Wang,Z Ye,... - 《Frontiers in Computational Neuroscience》 被引量: 0发表...
SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction Liushuai Shi1 Le Wang2* Chengjiang Long3 Sanping Zhou2 Mo Zhou2 Zhenxing Niu4 Gang Hua5 1School of Software Engineering, Xi'an Jiaotong University 2Institute of Artificial Intelligence and Robotic...
Here we combine several recent advances in graph neural network design to demonstrate that competitive hierarchical graph classification results are possible without sacrificing sparsity. 虽然分类的accuracy没有Diffpool高但是memory usage却低了不少,这在解决large-scale graph 有一定的优势 主要方法...
官网链接:GitHub - pyg-team/pytorch_geometric: Graph Neural Network Library for PyTorch 2022-08-30 回复1 tekila181122 tekila181122 点进这个github,找到Additional Libraries下面对应的链接,根据自己的系统版本等选择自己需要的whl下载,再手动安装即可。 2023-07-18 回复喜欢 tekila181122 太...
In this work, we proposed a novel dynamic graph neural network, Sparse-Dyn. It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure. Therefore, while avoiding the use of snapshots which causes information loss, it also achieves a...
Double-Branch Multi-Attention based Graph Neural Network for Knowledge Graph Completion Graph neural networks (GNNs), which effectively use topological structures in the knowledge graphs (KG) to embed entities and relations in low-dimensional ... H Xu,J Bao,W Liu 被引量: 0发表: 2023年 Parallel...
c In the scalable version of EN, EN-S regularizes DNN using only a few number of uniformly subsampled sequences from the combinatorial input space that casts the sparse WH recovery problem on an induced sparse-graph code. EN-S iterates between these two subproblems until convergence: (1) ...
【NeurIPS 2019】图神经网络(GNN)论文Approximation Ratios of Graph Neural Networks for Combinatorial Problems 采用使用神经网络从二维图像预测的三维数据以用于3D建模应用 用于解码神经网络的权重参数的解码器、编码器、方法和使用概率估计参数的编码表示 用于神经网络的量化 神经网络架构搜索的自动化 (信号与信息处理专业...