对于初始条件,x(u,0)=x0(t);为简单起见,我们假设没有边界条件。扩散系数函数的选择决定了扩散是均匀g=c、不均匀g(u,t)还是各向异性A(u,t)。在各向同性的情况下,扩散方程可以表示为 Graph diffusion equation.我们考虑以下在图上的扩散方程 这里我们用G=diag(a(xi(t),xj(t),t))表示一个对角矩阵,a是...
GRAND: Graph Neural Diffusion Chamberlain et al.: http://t.cn/A6f29cis http://t.cn/z8AbqMK
GRAND: Unbiased Connectome Atlas of Brain Network by Groupwise Graph Shrinkage and Network DiffusionThe original version of the chapter "GRAND: Unbiased Connectome Atlas of Brain Network by Groupwise G raph Shrink age and Network Diffusion", starting on p.127 was revised. An acknowledgment has......
Traffic Speed BJER4 PeMSD7(M) PeMSD7(L) STGCN Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting TF Mxnet Pytorch1 Pytorch2 Pytorch3 IJCAI 2018 Traffic Speed METR-LA PeMS-BAY DCRNN Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic For...
[156,158], principal curve or graph fitting [159–161], or random walks and diffusion operations on graphs ([162,163] among others) are used to infer pseudotime and/or branching trajectories. Alternative probabilistic descriptions can be obtained using optimal transport analysis [164] or ...
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The original version of the chapter "GRAND: Unbiased Connectome Atlas of Brain Network by Groupwise Graph Shrinkage and Network Diffusion", starting on p.127 was revised. An acknowledgment has been added. The original chapter was corrected.