gammagl.layers.conv.JumpingKnowledge

class JumpingKnowledge(mode, channels=None, num_layers=None)[source]

The Jumping Knowledge layer aggregation module from the “Representation Learning on Graphs with Jumping Knowledge Networks” paper based on either

  • concatenation ("cat")

\[\mathbf{x}_v^{(1)} \, \Vert \, \ldots \, \Vert \, \mathbf{x}_v^{(T)}\]

  • max pooling ("max")

\[\max \left( \mathbf{x}_v^{(1)}, \ldots, \mathbf{x}_v^{(T)} \right)\]

  • weighted summation

\[\sum_{t=1}^T \alpha_v^{(t)} \mathbf{x}_v^{(t)}\]

with attention scores \(\alpha_v^{(t)}\) obtained from a bi-directional LSTM ("lstm").

Parameters:

  • mode (str) – The aggregation scheme to use ("cat", "max" or "lstm").

  • channels (int, optional) – The number of channels per representation. Needs to be only set for LSTM-style aggregation. (default: None)

  • num_layers (int, optional) – The number of layers to aggregate. Needs to be only set for LSTM-style aggregation. (default: None)

forward(xs)[source]

Aggregates representations across different layers.

Parameters:

xs (list, tuple) – List containing layer-wise representations.