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utils.py
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utils.py
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"""Helper functions for graph extraction."""
import numpy as np
import scipy.sparse as ssp
import torch
from torch_geometric.data import Data
from torch_geometric.utils import (
negative_sampling, add_self_loops, to_undirected,
coalesce, remove_self_loops, scatter
)
def neighbors(fringe, A, outgoing=True):
if outgoing:
res = set(A[list(fringe)].indices)
else:
res = set(A[:, list(fringe)].indices)
return res
def k_hop_subgraph(src, dst, A, node_features=None, node_pos=None, y=1, hops=None):
nodes = [src, dst]
dists = [0, 0]
visited = set([src, dst])
fringe = set([src, dst])
for dist in range(1, hops+1):
fringe = neighbors(fringe, A)
fringe = fringe - visited
visited = visited.union(fringe)
if len(fringe) == 0:
break
nodes = nodes + list(fringe)
dists = dists + [dist] * len(fringe)
# Create subgraph and add target link
subgraph = A[nodes, :][:, nodes]
subgraph[0, 1] = 1
subgraph[1, 0] = 1
if node_features is not None:
node_features = node_features[nodes]
# Create data obj
subgraph = ssp.triu(subgraph) # make graph directed before line graph generations
u, v, _ = ssp.find(subgraph)
edge_index = torch.stack([torch.tensor(u).long(), torch.tensor(v).long()], 0)
src_edges = (edge_index == 0).any(dim=0).int()
dst_edges = (edge_index == 1).any(dim=0).int()
num_nodes = len(nodes)
y = torch.tensor([y])
edge_attr = node_features[edge_index[1]] - node_features[edge_index[0]]
edge_attr /= 0.01
edge_attr = torch.cat((edge_attr, src_edges.unsqueeze(1), dst_edges.unsqueeze(1)), dim=-1)
data = Data(x=node_features, edge_index=edge_index, edge_attr=edge_attr, num_nodes=num_nodes, y=y)
data_line_graph = gen_line_graph(data.clone())
if data_line_graph.is_directed():
data_line_graph.edge_index = to_undirected(data_line_graph.edge_index)
if data_line_graph.x.shape[0] == 1: # add self loop to graphs of originally 2 nodes / 1 edge
data_line_graph.edge_index = torch.tensor([[0], [0]])
assert data_line_graph.x.shape[0] == data_line_graph.edge_index.unique().shape[0]
return data_line_graph
def gen_line_graph(data):
N = data.num_nodes
edge_index, edge_attr = data.edge_index, data.edge_attr
edge_index_orig, edge_attr_orig = coalesce(edge_index, edge_attr, N)
edge_index = to_undirected(edge_index_orig)
row, col = edge_index
# Compute node indices
mask = row < col
row, col = row[mask], col[mask]
i = torch.arange(row.size(0), dtype=torch.long, device=row.device)
(row, col), i = coalesce(
torch.stack([
torch.cat([row, col], dim=0),
torch.cat([col, row], dim=0)
], dim=0),
torch.cat([i, i], dim=0),
N,
)
# Compute new edge indices according to `i`.
count = scatter(torch.ones_like(row), row, dim=0,
dim_size=data.num_nodes, reduce='sum')
joints = torch.split(i, count.tolist())
def generate_grid(x):
row = x.view(-1, 1).repeat(1, x.numel()).view(-1)
col = x.repeat(x.numel())
return torch.stack([row, col], dim=0)
joints = [generate_grid(joint) for joint in joints]
joints = torch.cat(joints, dim=1)
joints, _ = remove_self_loops(joints)
N = row.size(0) // 2
joints = coalesce(joints, num_nodes=N)
edge_index = joints.sort(dim=0)[0].unique(dim=1)
data.x = edge_attr_orig
data.edge_index = edge_index
data.num_nodes = edge_attr_orig.size(0)
data.edge_attr = None
assert data.x.shape[0] == data.num_nodes
return data
def get_pos_neg_edges(split, split_edge, edge_index, num_nodes, percent=100):
if 'edge' in split_edge['train']:
pos_edge = split_edge[split]['edge'].t()
if 'edge_neg' in split_edge['train']:
# use presampled negative training edges for ogbl-vessel
neg_edge = split_edge[split]['edge_neg'].t()
else:
new_edge_index, _ = add_self_loops(edge_index)
neg_edge = negative_sampling(
new_edge_index, num_nodes=num_nodes,
num_neg_samples=pos_edge.size(1))
# subsample for pos_edge
np.random.seed(123)
num_pos = pos_edge.size(1)
perm = np.random.permutation(num_pos)
perm = perm[:int(percent / 100 * num_pos)]
pos_edge = pos_edge[:, perm]
# subsample for neg_edge
np.random.seed(123)
num_neg = neg_edge.size(1)
perm = np.random.permutation(num_neg)
perm = perm[:int(percent / 100 * num_neg)]
neg_edge = neg_edge[:, perm]
elif 'source_node' in split_edge['train']:
source = split_edge[split]['source_node']
target = split_edge[split]['target_node']
if split == 'train':
target_neg = torch.randint(0, num_nodes, [target.size(0), 1],
dtype=torch.long)
else:
target_neg = split_edge[split]['target_node_neg']
# subsample
np.random.seed(123)
num_source = source.size(0)
perm = np.random.permutation(num_source)
perm = perm[:int(percent / 100 * num_source)]
source, target, target_neg = source[perm], target[perm], target_neg[perm, :]
pos_edge = torch.stack([source, target])
neg_per_target = target_neg.size(1)
neg_edge = torch.stack([source.repeat_interleave(neg_per_target),
target_neg.view(-1)])
return pos_edge, neg_edge