To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested ...
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Fig. 4. Architecture of a functional connectivity based spatial attention (FC-SAtt) module. 3.4. Spatial attention graph pooling The FC-SAtt module may generate the spatial attention map different from one sample to the other. This may increase the training difficulty in the following layers due...
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...
Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity ...
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...
we also integrate the linear capture module with a nonlinear neural network in parallel, resulting in our proposed graph and temporal convolution-linear capture networks (GTCLNs). We conduct experiments on the real-world datasets collected in Suzhou utility tunnels. The experimental results surpass the...
stress values for the spinner were in the same order of magnitude, the computed value for the shaker was one order of magnitude higher compared to the reported data27, which might be due to the finite element meshes and flow conditions used in the computational fluid dynamics (CFD) module. ...
The spatio-temporal fusion module consists of the node-specific GCN and the contiguous temporal learning module. For node-specific GCN, we assign a learnable weight matrix to each node (traffic series) to learn the specific traffic pattern of each node. Then, the predefined adjacent matrix is ...
The version embedding module introduced in the previous section transforms geographical entities into versions, reducing spatio-temporal knowledge quadruplets to triplets. Consequently, in this section, we augment the scoring function of the triplet embedding method introduced in the previous section with ...