# Siamese_ChannelIndependentConv¶

class src.gconv.Siamese_ChannelIndependentConv(in_features, num_features, in_edges, out_edges=None)[source]

Siamese Channel Independent Conv neural network for processing arbitrary number of graphs.

Parameters
• in_features – the dimension of input node features

• num_features – the dimension of output node features

• in_edges – the dimension of input edge features

• out_edges – (optional) the dimension of output edge features. It needs to be the same as num_features

forward(g1: Tuple[torch.Tensor, torch.Tensor, Optional[bool]], *args) List[torch.Tensor][source]

Forward computation of Siamese Channel Independent Conv.

Parameters
• g1 – The first graph, which is a tuple of ($$(b\times n\times n)$$ {0,1} adjacency matrix, $$(b\times n\times d_n)$$ input node embedding, $$(b\times n\times n\times d_e)$$ input edge embedding, mode (1 or 2))

• args – Other graphs

Returns

A list of tensors composed of new node embeddings $$(b\times n\times d^\prime)$$, appended with new edge embeddings $$(b\times n\times n\times d^\prime)$$

training: bool