gammagl.layers.conv.GPRConv

class GPRConv(K, alpha, Init='PPR', Gamma=None, **kwargs)

The graph propagation oeprator from the “Adaptive Universal Generalized PageRank Graph Neural Network” paper

\[\begin{split}\mathbf{H}^{(k)} = \mathbf{\hat{D}}^{-1/2} \mathbf{\hat{A}} \mathbf{\hat{D}}^{-1/2} \mathbf{H}^{(k-1)} \\ \mathbf{Z} = \sum\limits_{k=0}^{k=K}\gamma_k\mathbf{H}^{(k)}.\end{split}\]

where \(\mathbf{\hat{A}} = \mathbf{A} + \mathbf{I}\) denotes the adjacency matrix with inserted self-loops and \(\hat{D}_{ii} = \sum_{j=0} \hat{A}_{ij}\) its diagonal degree matrix. The adjacency matrix can include other values than 1 representing edge weights via the optional edge_weight tensor. \(\gamma_{k}\) its learnt weights.

Parameters:

• k – steps to propagate.

• alpha – assgin initial value to learnt weights, used in concert with Init.

• Init – initialization method(SGC, PPR, NPPR, Random, WS).

reset_parameters()

forward(x, edge_index, edge_weight=None, num_nodes=None)