easygraph.nn.convs.hypergraphs.halfnlh_conv module

class easygraph.nn.convs.hypergraphs.halfnlh_conv.HalfNLHconv(*args: Any, **kwargs: Any)

Bases: MessagePassing

The HalfNLHconv model proposed in YOU ARE ALLSET: A MULTISET LEARNING FRAMEWORK FOR HYPERGRAPH NEURAL NETWORKS paper (ICLR 2022).

Parameters:

• in_dim (int) – : The dimension of input.

• hid_dim (int) – : The dimension of hidden.

• out_dim (int) – : The dimension of output.

• num_layers (int) – : The number of layers.

• dropout (float) – Dropout ratio. Defaults to 0.5.

• normalization (str) – The normalization method. Defaults to bn

• InputNorm (bool) – Defaults to False.

• heads (int) – Defaults to 1

• attention` (bool) – Defaults to True

aggregate(inputs, index, dim_size=None, aggr='sum')

Aggregates messages from neighbors as \(\square_{j \in \mathcal{N}(i)}\).

Takes in the output of message computation as first argument and any argument which was initially passed to propagate().

By default, this function will delegate its call to scatter functions that support “add”, “mean” and “max” operations as specified in __init__() by the aggr argument.

forward(x, edge_index, norm, aggr='add')

input -> MLP -> Prop

message(x_j, norm)

reset_parameters()