def nx_random_features(g: nx.DiGraph, n_feat: int, e_feat: int, g_feat: int):
for _, ndata in g.nodes(data=True):
ndata["features"] = torch.randn(n_feat)
for _, _, edata in g.edges(data=True):
edata["features"] = torch.randn(e_feat)
g.data = {"features": torch.randn(g_feat)}
return g
def _default_g(g: nx.DiGraph):
for _, data in g.nodes(data=True):
data["features"] = np.zeros((1, ))
data["target"] = np.zeros((1, ))
for _, _, data in g.edges(data=True):
data["features"] = np.zeros((1, ))
data["target"] = np.zeros((1, ))
g.data = {"features": np.zeros((1, )), "target": np.zeros((1, ))}
return g
