torch_geometric_signed_directed.nn.directed.MagNetConv

Classes

MagNetConv

The magnetic graph convolutional operator from the

Module Contents

class MagNetConv(in_channels: int, out_channels: int, K: int, q: float, trainable_q: bool, normalization: str = 'sym', cached: bool = False, bias: bool = True, **kwargs)

Bases: torch_geometric.nn.conv.MessagePassing

The magnetic graph convolutional operator from the MagNet: A Neural Network for Directed Graphs. paper \(\mathbf{\hat{L}}\) denotes the scaled and normalized magnetic Laplacian \(\frac{2\mathbf{L}}{\lambda_{\max}} - \mathbf{I}\).

Parameters:

  • in_channels (int) – Size of each input sample.

  • out_channels (int) – Size of each output sample.

  • K (int) – Order of the Chebyshev polynomial, i.e., Chebyshev filter size minus 1 \(K\).

  • q (float, optional) – Initial value of the phase parameter, 0 <= q <= 0.25. Default: 0.25.

  • trainable_q (bool, optional) – whether to set q to be trainable or not. (default: False)

  • normalization (str, optional) – The normalization scheme for the magnetic Laplacian (default: sym): 1. None: No normalization \(\mathbf{L} = \mathbf{D} - \mathbf{A} \odot \exp(i \Theta^{(q)})\) 2. "sym": Symmetric normalization \(\mathbf{L} = \mathbf{I} - \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2} \odot \exp(i \Theta^{(q)})\) odot denotes the element-wise multiplication.

  • cached (bool, optional) – If set to True, the layer will cache the __norm__ matrix on first execution, and will use the cached version for further executions. This parameter should only be set to True in transductive learning scenarios. (default: False)

  • bias (bool, optional) – If set to False, the layer will not learn an additive bias. (default: True)

  • **kwargs (optional) – Additional arguments of torch_geometric.nn.conv.MessagePassing.

in_channels
out_channels
normalization = 'sym'
cached = False
trainable_q
weight
reset_parameters()
forward(x_real: torch.FloatTensor, x_imag: torch.FloatTensor, edge_index: torch.LongTensor, edge_weight: torch_geometric.typing.OptTensor = None, lambda_max: torch_geometric.typing.OptTensor = None) → torch.FloatTensor

Making a forward pass of the MagNet Convolution layer.

Arg types:

  • x_real, x_imag (PyTorch Float Tensor) - Node features.

  • edge_index (PyTorch Long Tensor) - Edge indices.

  • edge_weight (PyTorch Float Tensor, optional) - Edge weights corresponding to edge indices.

  • lambda_max (optional, but mandatory if normalization is None) - Largest eigenvalue of Laplacian.

Return types:

  • out_real, out_imag (PyTorch Float Tensor) - Hidden state tensor for all nodes, with shape (N_nodes, F_out).

message(x_j, norm)