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