def __init__(self, dim_node_features, dim_edge_features, dim_target,
predictor_class, config):
"""
Initializes the model.
:param dim_node_features: arbitrary object holding node feature information
:param dim_edge_features: arbitrary object holding edge feature information
:param dim_target: arbitrary object holding target information
:param predictor_class: the class of the predictor that will classify node/graph embeddings produced by this DGN
:param config: the configuration dictionary to extract further hyper-parameters
"""
super().__init__()
num_layers = config['num_layers']
dim_embedding = config['dim_embedding']
self.aggregation = config['aggregation'] # can be mean or max
if self.aggregation == 'max':
self.fc_max = nn.Linear(dim_embedding, dim_embedding)
self.predictor = predictor_class(dim_node_features=dim_embedding *
num_layers,
dim_edge_features=dim_edge_features,
dim_target=dim_target,
config=config)
self.layers = nn.ModuleList([])
for i in range(num_layers):
dim_input = dim_node_features if i == 0 else dim_embedding
conv = SAGEConv(dim_input, dim_embedding)
# Overwrite aggregation method (default is set to mean
conv.aggr = self.aggregation
self.layers.append(conv)
def __init__(self, dim_features, dim_target, config):
super().__init__()
num_layers = config['num_layers']
dim_embedding = config['dim_embedding']
self.aggregation = config['aggregation'] # can be mean or max
if self.aggregation == 'max':
self.fc_max = nn.Linear(dim_embedding, dim_embedding)
self.layers = nn.ModuleList([])
for i in range(num_layers):
dim_input = dim_features if i == 0 else dim_embedding
conv = SAGEConv(dim_input, dim_embedding)
# Overwrite aggregation method (default is set to mean
conv.aggr = self.aggregation
self.layers.append(conv)
# For graph classification
self.fc1 = nn.Linear(num_layers * dim_embedding, dim_embedding)
self.fc2 = nn.Linear(dim_embedding, dim_target)
def __init__(self,
n_feat,
n_class,
n_layer,
agg_hidden,
fc_hidden,
dropout,
readout,
device,
aggregation='mean'):
super(GraphSAGE, self).__init__()
self.n_layer = n_layer
self.dropout = dropout
self.readout = readout
self.aggregation = aggregation
self.device = device
self.readout_dim = agg_hidden * n_layer
# Graph sage layer
self.graph_sage_layers = []
for i in range(n_layer):
if i == 0:
sage = SAGEConv(n_feat, agg_hidden).to(device)
else:
sage = SAGEConv(agg_hidden, agg_hidden).to(device)
sage.aggr = self.aggregation
self.graph_sage_layers.append(sage)
if self.aggregation == 'max':
self.fc_max = nn.Linear(agg_hidden, agg_hidden)
# Fully-connected layer
self.fc1 = nn.Linear(self.readout_dim, fc_hidden)
self.fc2 = nn.Linear(fc_hidden, n_class)