graphstorm.model.RelGraphConvLayer

class graphstorm.model.RelGraphConvLayer(in_feat, out_feat, rel_names, num_bases, *, weight=True, bias=True, activation=None, self_loop=False, dropout=0.0, num_ffn_layers_in_gnn=0, ffn_activation=<function relu>, norm=None)

Relational graph convolution layer from Modeling Relational Data with Graph Convolutional Networks.

A generic module for computing convolution on heterogeneous graphs.

The relational graph convolution layer applies GraphConv on the relation graphs, which reads the features from source nodes and writes the updated ones to destination nodes. If multiple relations have the same destination node types, their results are aggregated by the specified method. If the relation graph has no edge, the corresponding module will not be called.

Mathematically for the GraphConv it is defined as follows:

\[h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{1}{c_{ji}}h_j^{(l)}W^{(l)})\]

where \(\mathcal{N}(i)\) is the set of neighbors of node \(i\), \(c_{ji}\) is the product of the square root of node degrees (i.e., \(c_{ji} = \sqrt{|\mathcal{N}(j)|}\sqrt{|\mathcal{N}(i)|}\)), and \(\sigma\) is an activation function.

If a weight tensor on each edge is provided, the weighted graph convolution is defined as:

\[h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{e_{ji}}{c_{ji}}h_j^{(l)}W^{(l)})\]

where \(e_{ji}\) is the scalar weight on the edge from node \(j\) to node \(i\). This is NOT equivalent to the weighted graph convolutional network formulation in the paper.

Note:

• In the RelGraphConvLayer, the implementation select ‘right’ or the default option for the norm

to divide the aggregated messages by each node’s in-degrees, which is equivalent to averaging the received messages.

Examples:

# suppose graph and input_feature are ready
from graphstorm.model.rgcn_encoder import RelGraphConvLayer

layer = RelGraphConvLayer(
        h_dim, h_dim, g.canonical_etypes,
        num_bases, activation, self_loop,
        dropout, num_ffn_layers_in_gnn,
        ffn_activation, norm)
h = layer(g, input_feature)

Parameters

in_featint

Input feature size.

out_featint

Output feature size.

rel_nameslist[str]

Relation names.

num_basesint

Number of bases. If is none, use number of relations. Default: None.

weightbool, optional

True if a linear layer is applied after message passing. Default: True

biasbool, optional

True if bias is added. Default: True

activationcallable, optional

Activation function. Default: None

self_loopbool, optional

True to include self loop message. Default: False

dropoutfloat, optional

Dropout rate. Default: 0.0

num_ffn_layers_in_gnn: int, optional

Number of layers of ngnn between gnn layers

ffn_actication: torch.nn.functional

Activation Method for ngnn

normstr, optional

Normalization Method. Default: None