def __init__(
self,
dataset,
rw_hops=64,
subgraph_size=64,
restart_prob=0.8,
positional_embedding_size=32,
step_dist=[1.0, 0.0, 0.0],
):
self.rw_hops = rw_hops
self.subgraph_size = subgraph_size
self.restart_prob = restart_prob
self.positional_embedding_size = positional_embedding_size
self.step_dist = step_dist
assert positional_embedding_size > 1
if dataset == "motif":
self.graphs = self._create_dgl_graph2()
else:
self.data = data_util.create_node_classification_dataset(dataset).data
print(self.data)
self.graphs = [self._create_dgl_graph(self.data)]
print(self.graphs)
#exit(0)
self.length = sum([g.number_of_nodes() for g in self.graphs])
self.total = self.length
def __init__(self, dataset, model, hidden_size, num_shuffle, seed,
**model_args):
self.data = create_node_classification_dataset(dataset).data
self.label_matrix = self.data.y
self.num_nodes, self.num_classes = self.data.y.shape
self.model = build_model(model, hidden_size, **model_args)
self.hidden_size = hidden_size
self.num_shuffle = num_shuffle
self.seed = seed