def __init__(self, in_feats, out_feats, normalize=False, aggr="mean"):
super(GraphSAGELayer, self).__init__()
self.in_feats = in_feats
self.out_feats = out_feats
self.normalize = normalize
if aggr == "mean":
self.aggr = MeanAggregator(in_feats, out_feats)
elif aggr == "sum":
self.aggr = SumAggregator(in_feats, out_feats)
else:
raise NotImplementedError
def __init__(self, num_features, num_classes, hidden_size, num_layers,
sample_size, dropout):
super(Graphsage, self).__init__()
self.adjlist = {}
self.num_features = num_features
self.num_classes = num_classes
self.hidden_size = hidden_size
self.num_layers = num_layers
self.sample_size = sample_size
self.dropout = dropout
shapes = [num_features] + hidden_size + [num_classes]
print(shapes)
self.convs = nn.ModuleList([
MeanAggregator(shapes[layer], shapes[layer + 1], cached=True)
for layer in range(num_layers)
])
def __init__(self, in_feats, out_feats):
super(GraphSAGELayer, self).__init__()
self.in_feats = in_feats
self.out_feats = out_feats
self.aggr = MeanAggregator(in_feats, out_feats, cached=True)