gammagl.layers.conv.MGNNI_m_iter¶
- class MGNNI_m_iter(m, k, threshold, max_iter, gamma, layer_norm=False)[source]¶
The mgnni operator from the “Multiscale Graph Neural Networks with Implicit Layers” paper
\[Z^{(l+1)} =\gamma g(F)Z^{(l)}S^{(m)}+f(X;G)\]where :math gamma denotes the contraction factor, :math m denotes a hyperparameter for graph scale(i.e., the power of adjacency matrix) and :math f(X;G) is a parameterized transformation on input features and graphs, the normalized weight matrix \(g(F)\) are computed as
\[g(F) =\frac{1}{\|F^\top F\|_\text{F}+\epsilon_F}F^\top F\]- Parameters:
m (int) – Size of each input sample to derive the size from the first input(s) to the forward method.
k (int) – The power of adjacency matrix. The greater the k, the further the distance to capture the information
threshold (int) – Threshold for convergence. Convergence is considered when the difference between the two times is less than this threshold
max_iter (int) – Maximum number of iterative solver iterations
gamma (float) – The contraction factor. The smaller the gamma, the faster the contraction, the smaller the capture range; the larger the gamma, the larger the capture range, but it is difficult to converge and inefficient
layer_norm (bool, optional) – whether to use layer norm. (default:
False)