Cheby Graph Convolution classChebGraphConv(nn.Module):def__init__(self,c_in,c_out,Ks,gso,bias):super(ChebGraphConv,self).__init__()self.c_in=c_inself.c_out=c_outself.Ks=Ksself.gso=gsoself.weight=nn.Parameter(torch.FloatTensor(Ks,c_in,c_out))ifbias:self.bias=nn.Parameter(torch...
First, we propose a spatio-temporal node embedding representation module to capture the traffic patterns of different stations. Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix. Finally,...
Short-Term Marine Wind Speed Forecasting Based on Dynamic Graph Embedding and Spatiotemporal Information The graph neural network and LSTM module are sequentially interconnected to collectively capture spatio-temporal correlations. Ultimately, the multi-step ... D Dong,S Wang,Q Guo,... 被引量: 0...
Concerning the data sources, we distinguish between infection and Facebook data on human mobility and connectedness. While the infection data are time series solely utilized in the model’s structured and target component, most network data is used directly in the GNN module. To allow for sanity ...
此文提出了一个新的动态图生成算法建模路段间动态的空间关系,这个新的graph constructor受到tensordecomposition的启发并且考虑到了交通数据周期性的动态变化。 此文设计了一个新的 multi-faceted fusion module 去时空两方面集成主要的交通速度特征与辅助的其他交通特征,它可以被使用于其他的时空预测任务。
Knowledge base graph embedding module design for Visual question answering model Pattern Recogn., 120 (2021), Article 108153 Google Scholar Zhou et al., 2021 C. Zhou, H. Wang, C. Wang, et al. Geoscience knowledge graph in the big data era Sci. China Earth Sci., 64 (7) (2021), pp...
Firstly, a spatio-temporal graph is constructed with three different nodes, including airport, route, and fix to describe the topology structure of MAS. Secondly, a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of ...
基于骨骼动作识别的解耦时空注意网络 paper:https://arxiv.org/abs/2007.03263 文章目录 Abstract Introduction Method 3.1 Spatial-temporal attention module 3.2 Decoupled Position encoding 3.3 Spatial global regularization 3.4 C... 论文阅读:An attention enhanced graph convolutional lstm network for skeleton-based...
(iv) Graph Multi-Attention Network model The GMAN model is an encoder–decoder architecture that integrates an attention mechanism, including the ST-Attention module (STAtt) with residual connections [37]. GMAN incorporates graph structure and temporal information into a multi-attention mechanism through...
External Module:这个负责外部扰动和特殊情况对交通数据的影响,比如节日和极端天气这种特殊情况。 三个模块的信息通过GCN层结合在一起,生成一个预测输出。 判别器部分: 时空鉴别器由GCN层和GCGRU层组成,分别对当前时间和时空特征进行建模 在对抗训练中,判别器可以促进生成器的学习,并使用生成的负数据进行训练。