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|
import torch
from torch_geometric.datasets import KarateClub
from torch_geometric.testing import withPackage
from torch_geometric.transforms import GDC
from torch_geometric.utils import to_dense_adj
@withPackage('numba')
def test_gdc():
data = KarateClub()[0]
gdc = GDC(
self_loop_weight=1,
normalization_in='sym',
normalization_out='sym',
diffusion_kwargs=dict(method='ppr', alpha=0.15),
sparsification_kwargs=dict(method='threshold', avg_degree=2),
exact=True,
)
out = gdc(data)
mat = to_dense_adj(out.edge_index, edge_attr=out.edge_attr).squeeze()
assert torch.all(mat >= -1e-8)
assert torch.allclose(mat, mat.t(), atol=1e-4)
gdc = GDC(
self_loop_weight=1,
normalization_in='sym',
normalization_out='sym',
diffusion_kwargs=dict(method='heat', t=10),
sparsification_kwargs=dict(method='threshold', avg_degree=2),
exact=True,
)
out = gdc(data)
mat = to_dense_adj(out.edge_index, edge_attr=out.edge_attr).squeeze()
assert torch.all(mat >= -1e-8)
assert torch.allclose(mat, mat.t(), atol=1e-4)
gdc = GDC(
self_loop_weight=1,
normalization_in='col',
normalization_out='col',
diffusion_kwargs=dict(method='heat', t=10),
sparsification_kwargs=dict(method='topk', k=2, dim=0),
exact=True,
)
out = gdc(data)
mat = to_dense_adj(out.edge_index, edge_attr=out.edge_attr).squeeze()
col_sum = mat.sum(0)
assert torch.all(mat >= -1e-8)
assert torch.all(
torch.isclose(col_sum, torch.tensor(1.0))
| torch.isclose(col_sum, torch.tensor(0.0)))
assert torch.all((~torch.isclose(mat, torch.tensor(0.0))).sum(0) == 2)
gdc = GDC(
self_loop_weight=1,
normalization_in='row',
normalization_out='row',
diffusion_kwargs=dict(method='heat', t=5),
sparsification_kwargs=dict(method='topk', k=2, dim=1),
exact=True,
)
out = gdc(data)
mat = to_dense_adj(out.edge_index, edge_attr=out.edge_attr).squeeze()
row_sum = mat.sum(1)
assert torch.all(mat >= -1e-8)
assert torch.all(
torch.isclose(row_sum, torch.tensor(1.0))
| torch.isclose(row_sum, torch.tensor(0.0)))
assert torch.all((~torch.isclose(mat, torch.tensor(0.0))).sum(1) == 2)
gdc = GDC(
self_loop_weight=1,
normalization_in='row',
normalization_out='row',
diffusion_kwargs=dict(method='coeff', coeffs=[0.8, 0.3, 0.1]),
sparsification_kwargs=dict(method='threshold', eps=0.1),
exact=True,
)
out = gdc(data)
mat = to_dense_adj(out.edge_index, edge_attr=out.edge_attr).squeeze()
row_sum = mat.sum(1)
assert torch.all(mat >= -1e-8)
assert torch.all(
torch.isclose(row_sum, torch.tensor(1.0))
| torch.isclose(row_sum, torch.tensor(0.0)))
gdc = GDC(
self_loop_weight=1,
normalization_in='sym',
normalization_out='col',
diffusion_kwargs=dict(method='ppr', alpha=0.15, eps=1e-4),
sparsification_kwargs=dict(method='threshold', avg_degree=2),
exact=False,
)
out = gdc(data)
mat = to_dense_adj(out.edge_index, edge_attr=out.edge_attr).squeeze()
col_sum = mat.sum(0)
assert torch.all(mat >= -1e-8)
assert torch.all(
torch.isclose(col_sum, torch.tensor(1.0))
| torch.isclose(col_sum, torch.tensor(0.0)))
|
