"""Synthetic graph datasets."""
import math
import networkx as nx
import numpy as np
import os
import pickle
import random
from .dgl_dataset import DGLBuiltinDataset
from .utils import save_graphs, load_graphs, _get_dgl_url, download
from .. import backend as F
from ..batch import batch
from ..convert import graph
from ..transforms import reorder_graph
[docs]class BAShapeDataset(DGLBuiltinDataset):
r"""BA-SHAPES dataset from `GNNExplainer: Generating Explanations for Graph Neural Networks
<https://arxiv.org/abs/1903.03894>`__
This is a synthetic dataset for node classification. It is generated by performing the
following steps in order.
- Construct a base Barabási–Albert (BA) graph.
- Construct a set of five-node house-structured network motifs.
- Attach the motifs to randomly selected nodes of the base graph.
- Perturb the graph by adding random edges.
- Nodes are assigned to 4 classes. Nodes of label 0 belong to the base BA graph. Nodes of
label 1, 2, 3 are separately at the middle, bottom, or top of houses.
- Generate constant feature for all nodes, which is 1.
Parameters
----------
num_base_nodes : int, optional
Number of nodes in the base BA graph. Default: 300
num_base_edges_per_node : int, optional
Number of edges to attach from a new node to existing nodes in constructing the base BA
graph. Default: 5
num_motifs : int, optional
Number of house-structured network motifs to use. Default: 80
perturb_ratio : float, optional
Number of random edges to add in perturbation divided by the number of edges in the
original graph. Default: 0.01
seed : integer, random_state, or None, optional
Indicator of random number generation state. Default: None
raw_dir : str, optional
Raw file directory to store the processed data. Default: ~/.dgl/
force_reload : bool, optional
Whether to always generate the data from scratch rather than load a cached version.
Default: False
verbose : bool, optional
Whether to print progress information. Default: True
transform : callable, optional
A transform that takes in a :class:`~dgl.DGLGraph` object and returns
a transformed version. The :class:`~dgl.DGLGraph` object will be
transformed before every access. Default: None
Attributes
----------
num_classes : int
Number of node classes
Examples
--------
>>> from dgl.data import BAShapeDataset
>>> dataset = BAShapeDataset()
>>> dataset.num_classes
4
>>> g = dataset[0]
>>> label = g.ndata['label']
>>> feat = g.ndata['feat']
"""
def __init__(self,
num_base_nodes=300,
num_base_edges_per_node=5,
num_motifs=80,
perturb_ratio=0.01,
seed=None,
raw_dir=None,
force_reload=False,
verbose=True,
transform=None):
self.num_base_nodes = num_base_nodes
self.num_base_edges_per_node = num_base_edges_per_node
self.num_motifs = num_motifs
self.perturb_ratio = perturb_ratio
self.seed = seed
super(BAShapeDataset, self).__init__(name='BA-SHAPES',
url=None,
raw_dir=raw_dir,
force_reload=force_reload,
verbose=verbose,
transform=transform)
def process(self):
g = nx.barabasi_albert_graph(self.num_base_nodes, self.num_base_edges_per_node, self.seed)
edges = list(g.edges())
src, dst = map(list, zip(*edges))
n = self.num_base_nodes
# Nodes in the base BA graph belong to class 0
node_labels = [0] * n
# The motifs will be evenly attached to the nodes in the base graph.
spacing = math.floor(n / self.num_motifs)
for motif_id in range(self.num_motifs):
# Construct a five-node house-structured network motif
motif_edges = [
(n, n + 1),
(n + 1, n + 2),
(n + 2, n + 3),
(n + 3, n),
(n + 4, n),
(n + 4, n + 1)
]
motif_src, motif_dst = map(list, zip(*motif_edges))
src.extend(motif_src)
dst.extend(motif_dst)
# Nodes at the middle of a house belong to class 1
# Nodes at the bottom of a house belong to class 2
# Nodes at the top of a house belong to class 3
node_labels.extend([1, 1, 2, 2, 3])
# Attach the motif to the base BA graph
src.append(n)
dst.append(int(motif_id * spacing))
n += 5
g = graph((src, dst), num_nodes=n)
# Perturb the graph by adding non-self-loop random edges
num_real_edges = g.num_edges()
max_ratio = (n * (n - 1) - num_real_edges) / num_real_edges
assert self.perturb_ratio <= max_ratio, \
'perturb_ratio cannot exceed {:.4f}'.format(max_ratio)
num_random_edges = int(num_real_edges * self.perturb_ratio)
if self.seed is not None:
np.random.seed(self.seed)
for _ in range(num_random_edges):
while True:
u = np.random.randint(0, n)
v = np.random.randint(0, n)
if (not g.has_edges_between(u, v)) and (u != v):
break
g.add_edges(u, v)
g.ndata['label'] = F.tensor(node_labels, F.int64)
g.ndata['feat'] = F.ones((n, 1), F.float32, F.cpu())
self._graph = reorder_graph(
g, node_permute_algo='rcmk', edge_permute_algo='dst', store_ids=False)
@property
def graph_path(self):
return os.path.join(self.save_path, '{}_dgl_graph.bin'.format(self.name))
def save(self):
save_graphs(str(self.graph_path), self._graph)
def has_cache(self):
return os.path.exists(self.graph_path)
def load(self):
graphs, _ = load_graphs(str(self.graph_path))
self._graph = graphs[0]
[docs] def __getitem__(self, idx):
assert idx == 0, "This dataset has only one graph."
if self._transform is None:
return self._graph
else:
return self._transform(self._graph)
[docs] def __len__(self):
return 1
@property
def num_classes(self):
return 4
[docs]class TreeCycleDataset(DGLBuiltinDataset):
r"""TREE-CYCLES dataset from `GNNExplainer: Generating Explanations for Graph Neural Networks
<https://arxiv.org/abs/1903.03894>`__
This is a synthetic dataset for node classification. It is generated by performing the
following steps in order.
- Construct a balanced binary tree as the base graph.
- Construct a set of cycle motifs.
- Attach the motifs to randomly selected nodes of the base graph.
- Perturb the graph by adding random edges.
- Generate constant feature for all nodes, which is 1.
- Nodes in the tree belong to class 0 and nodes in cycles belong to class 1.
Parameters
----------
tree_height : int, optional
Height of the balanced binary tree. Default: 8
num_motifs : int, optional
Number of cycle motifs to use. Default: 60
cycle_size : int, optional
Number of nodes in a cycle motif. Default: 6
perturb_ratio : float, optional
Number of random edges to add in perturbation divided by the
number of original edges in the graph. Default: 0.01
seed : integer, random_state, or None, optional
Indicator of random number generation state. Default: None
raw_dir : str, optional
Raw file directory to store the processed data. Default: ~/.dgl/
force_reload : bool, optional
Whether to always generate the data from scratch rather than load a cached version.
Default: False
verbose : bool, optional
Whether to print progress information. Default: True
transform : callable, optional
A transform that takes in a :class:`~dgl.DGLGraph` object and returns
a transformed version. The :class:`~dgl.DGLGraph` object will be
transformed before every access. Default: None
Attributes
----------
num_classes : int
Number of node classes
Examples
--------
>>> from dgl.data import TreeCycleDataset
>>> dataset = TreeCycleDataset()
>>> dataset.num_classes
2
>>> g = dataset[0]
>>> label = g.ndata['label']
>>> feat = g.ndata['feat']
"""
def __init__(self,
tree_height=8,
num_motifs=60,
cycle_size=6,
perturb_ratio=0.01,
seed=None,
raw_dir=None,
force_reload=False,
verbose=True,
transform=None):
self.tree_height = tree_height
self.num_motifs = num_motifs
self.cycle_size = cycle_size
self.perturb_ratio = perturb_ratio
self.seed = seed
super(TreeCycleDataset, self).__init__(name='TREE-CYCLES',
url=None,
raw_dir=raw_dir,
force_reload=force_reload,
verbose=verbose,
transform=transform)
def process(self):
if self.seed is not None:
np.random.seed(self.seed)
g = nx.balanced_tree(r=2, h=self.tree_height)
edges = list(g.edges())
src, dst = map(list, zip(*edges))
n = nx.number_of_nodes(g)
# Nodes in the base tree graph belong to class 0
node_labels = [0] * n
# The motifs will be evenly attached to the nodes in the base graph.
spacing = math.floor(n / self.num_motifs)
for motif_id in range(self.num_motifs):
# Construct a six-node cycle
motif_edges = [(n + i, n + i + 1) for i in range(5)]
motif_edges.append((n + 5, n))
motif_src, motif_dst = map(list, zip(*motif_edges))
src.extend(motif_src)
dst.extend(motif_dst)
# Nodes in cycles belong to class 1
node_labels.extend([1] * self.cycle_size)
# Attach the motif to the base tree graph
anchor = int(motif_id * spacing)
src.append(n)
dst.append(anchor)
if np.random.random() > 0.5:
a = np.random.randint(1, 4)
b = np.random.randint(1, 4)
src.append(n + a)
dst.append(anchor + b)
n += self.cycle_size
g = graph((src, dst), num_nodes=n)
# Perturb the graph by adding non-self-loop random edges
num_real_edges = g.num_edges()
max_ratio = (n * (n - 1) - num_real_edges) / num_real_edges
assert self.perturb_ratio <= max_ratio, \
'perturb_ratio cannot exceed {:.4f}'.format(max_ratio)
num_random_edges = int(num_real_edges * self.perturb_ratio)
for _ in range(num_random_edges):
while True:
u = np.random.randint(0, n)
v = np.random.randint(0, n)
if (not g.has_edges_between(u, v)) and (u != v):
break
g.add_edges(u, v)
g.ndata['label'] = F.tensor(node_labels, F.int64)
g.ndata['feat'] = F.ones((n, 1), F.float32, F.cpu())
self._graph = reorder_graph(
g, node_permute_algo='rcmk', edge_permute_algo='dst', store_ids=False)
@property
def graph_path(self):
return os.path.join(self.save_path, '{}_dgl_graph.bin'.format(self.name))
def save(self):
save_graphs(str(self.graph_path), self._graph)
def has_cache(self):
return os.path.exists(self.graph_path)
def load(self):
graphs, _ = load_graphs(str(self.graph_path))
self._graph = graphs[0]
[docs] def __getitem__(self, idx):
assert idx == 0, "This dataset has only one graph."
if self._transform is None:
return self._graph
else:
return self._transform(self._graph)
[docs] def __len__(self):
return 1
@property
def num_classes(self):
return 2
[docs]class TreeGridDataset(DGLBuiltinDataset):
r"""TREE-GRIDS dataset from `GNNExplainer: Generating Explanations for Graph Neural Networks
<https://arxiv.org/abs/1903.03894>`__
This is a synthetic dataset for node classification. It is generated by performing the
following steps in order.
- Construct a balanced binary tree as the base graph.
- Construct a set of n-by-n grid motifs.
- Attach the motifs to randomly selected nodes of the base graph.
- Perturb the graph by adding random edges.
- Generate constant feature for all nodes, which is 1.
- Nodes in the tree belong to class 0 and nodes in grids belong to class 1.
Parameters
----------
tree_height : int, optional
Height of the balanced binary tree. Default: 8
num_motifs : int, optional
Number of grid motifs to use. Default: 80
grid_size : int, optional
The number of nodes in a grid motif will be grid_size ^ 2. Default: 3
perturb_ratio : float, optional
Number of random edges to add in perturbation divided by the
number of original edges in the graph. Default: 0.1
seed : integer, random_state, or None, optional
Indicator of random number generation state. Default: None
raw_dir : str, optional
Raw file directory to store the processed data. Default: ~/.dgl/
force_reload : bool, optional
Whether to always generate the data from scratch rather than load a cached version.
Default: False
verbose : bool, optional
Whether to print progress information. Default: True
transform : callable, optional
A transform that takes in a :class:`~dgl.DGLGraph` object and returns
a transformed version. The :class:`~dgl.DGLGraph` object will be
transformed before every access. Default: None
Attributes
----------
num_classes : int
Number of node classes
Examples
--------
>>> from dgl.data import TreeGridDataset
>>> dataset = TreeGridDataset()
>>> dataset.num_classes
2
>>> g = dataset[0]
>>> label = g.ndata['label']
>>> feat = g.ndata['feat']
"""
def __init__(self,
tree_height=8,
num_motifs=80,
grid_size=3,
perturb_ratio=0.1,
seed=None,
raw_dir=None,
force_reload=False,
verbose=True,
transform=None):
self.tree_height = tree_height
self.num_motifs = num_motifs
self.grid_size = grid_size
self.perturb_ratio = perturb_ratio
self.seed = seed
super(TreeGridDataset, self).__init__(name='TREE-GRIDS',
url=None,
raw_dir=raw_dir,
force_reload=force_reload,
verbose=verbose,
transform=transform)
def process(self):
if self.seed is not None:
np.random.seed(self.seed)
g = nx.balanced_tree(r=2, h=self.tree_height)
edges = list(g.edges())
src, dst = map(list, zip(*edges))
n = nx.number_of_nodes(g)
# Nodes in the base tree graph belong to class 0
node_labels = [0] * n
# The motifs will be evenly attached to the nodes in the base graph.
spacing = math.floor(n / self.num_motifs)
# Construct an n-by-n grid
motif_g = nx.grid_graph([self.grid_size, self.grid_size])
grid_size = nx.number_of_nodes(motif_g)
motif_g = nx.convert_node_labels_to_integers(motif_g, first_label=0)
motif_edges = list(motif_g.edges())
motif_src, motif_dst = map(list, zip(*motif_edges))
motif_src, motif_dst = np.array(motif_src), np.array(motif_dst)
for motif_id in range(self.num_motifs):
src.extend((motif_src + n).tolist())
dst.extend((motif_dst + n).tolist())
# Nodes in grids belong to class 1
node_labels.extend([1] * grid_size)
# Attach the motif to the base tree graph
src.append(n)
dst.append(int(motif_id * spacing))
n += grid_size
g = graph((src, dst), num_nodes=n)
# Perturb the graph by adding non-self-loop random edges
num_real_edges = g.num_edges()
max_ratio = (n * (n - 1) - num_real_edges) / num_real_edges
assert self.perturb_ratio <= max_ratio, \
'perturb_ratio cannot exceed {:.4f}'.format(max_ratio)
num_random_edges = int(num_real_edges * self.perturb_ratio)
for _ in range(num_random_edges):
while True:
u = np.random.randint(0, n)
v = np.random.randint(0, n)
if (not g.has_edges_between(u, v)) and (u != v):
break
g.add_edges(u, v)
g.ndata['label'] = F.tensor(node_labels, F.int64)
g.ndata['feat'] = F.ones((n, 1), F.float32, F.cpu())
self._graph = reorder_graph(
g, node_permute_algo='rcmk', edge_permute_algo='dst', store_ids=False)
@property
def graph_path(self):
return os.path.join(self.save_path, '{}_dgl_graph.bin'.format(self.name))
def save(self):
save_graphs(str(self.graph_path), self._graph)
def has_cache(self):
return os.path.exists(self.graph_path)
def load(self):
graphs, _ = load_graphs(str(self.graph_path))
self._graph = graphs[0]
[docs] def __getitem__(self, idx):
assert idx == 0, "This dataset has only one graph."
if self._transform is None:
return self._graph
else:
return self._transform(self._graph)
[docs] def __len__(self):
return 1
@property
def num_classes(self):
return 2
[docs]class BA2MotifDataset(DGLBuiltinDataset):
r"""BA-2motifs dataset from `Parameterized Explainer for Graph Neural Network
<https://arxiv.org/abs/2011.04573>`__
This is a synthetic dataset for graph classification. It was generated by
performing the following steps in order.
- Construct 1000 base Barabási–Albert (BA) graphs.
- Attach house-structured network motifs to half of the base BA graphs.
- Attach five-node cycle motifs to the rest base BA graphs.
- Assign each graph to one of two classes according to the type of the attached motif.
Parameters
----------
raw_dir : str, optional
Raw file directory to download and store the data. Default: ~/.dgl/
force_reload : bool, optional
Whether to reload the dataset. Default: False
verbose : bool, optional
Whether to print progress information. Default: True
transform : callable, optional
A transform that takes in a :class:`~dgl.DGLGraph` object and returns
a transformed version. The :class:`~dgl.DGLGraph` object will be
transformed before every access. Default: None
Attributes
----------
num_classes : int
Number of node classes
Examples
--------
>>> from dgl.data import BA2MotifDataset
>>> dataset = BA2MotifDataset()
>>> dataset.num_classes
2
>>> # Get the first graph and its label
>>> g, label = dataset[0]
>>> feat = g.ndata['feat']
"""
def __init__(self,
raw_dir=None,
force_reload=False,
verbose=True,
transform=None):
super(BA2MotifDataset, self).__init__(name='BA-2motifs',
url=_get_dgl_url('dataset/BA-2motif.pkl'),
raw_dir=raw_dir,
force_reload=force_reload,
verbose=verbose,
transform=transform)
def download(self):
r""" Automatically download data."""
file_path = os.path.join(self.raw_dir, self.name + '.pkl')
download(self.url, path=file_path)
def process(self):
file_path = os.path.join(self.raw_dir, self.name + '.pkl')
with open(file_path, 'rb') as f:
adjs, features, labels = pickle.load(f)
self.graphs = []
self.labels = F.tensor(labels, F.int64)
for i in range(len(adjs)):
g = graph(adjs[i].nonzero())
g.ndata['feat'] = F.zerocopy_from_numpy(features[i])
self.graphs.append(g)
@property
def graph_path(self):
return os.path.join(self.save_path, '{}_dgl_graph.bin'.format(self.name))
def save(self):
label_dict = {'labels': self.labels}
save_graphs(str(self.graph_path), self.graphs, label_dict)
def has_cache(self):
return os.path.exists(self.graph_path)
def load(self):
self.graphs, label_dict = load_graphs(str(self.graph_path))
self.labels = label_dict['labels']
[docs] def __getitem__(self, idx):
g = self.graphs[idx]
if self._transform is not None:
g = self._transform(g)
return g, self.labels[idx]
[docs] def __len__(self):
return len(self.graphs)
@property
def num_classes(self):
return 2