def __init__(self,
*graph,
n_clusters=None,
adj_transform="normalize_adj",
attr_transform=None,
device='cpu:0',
seed=None,
name=None,
**kwargs):
"""Create a Cluster Graph Convolutional Networks (ClusterGCN) model.
This can be instantiated in several ways:
model = ClusterGCN(graph)
with a `graphgallery.data.Graph` instance representing
A sparse, attributed, labeled graph.
model = ClusterGCN(adj_matrix, attr_matrix, labels)
where `adj_matrix` is a 2D Scipy sparse matrix denoting the graph,
`attr_matrix` is a 2D Numpy array-like matrix denoting the node
attributes, `labels` is a 1D Numpy array denoting the node labels.
Parameters:
----------
graph: An instance of `graphgallery.data.Graph` or a tuple (list) of inputs.
A sparse, attributed, labeled graph.
n_clusters: integer. optional
The number of clusters that the graph being seperated,
if not specified (`None`), it will be set to the number
of classes automatically. (default :obj: `None`).
adj_transform: string, `transform`, or None. optional
How to transform the adjacency matrix. See `graphgallery.transforms`
(default: :obj:`'normalize_adj'` with normalize rate `-0.5`.
i.e., math:: \hat{A} = D^{-\frac{1}{2}} A D^{-\frac{1}{2}})
attr_transform: string, `transform`, or None. optional
How to transform the node attribute matrix. See `graphgallery.transforms`
(default :obj: `None`)
device: string. optional
The device where the model is running on. You can specified `CPU` or `GPU`
for the model. (default: :str: `CPU:0`, i.e., running on the 0-th `CPU`)
seed: interger scalar. optional
Used in combination with `tf.random.set_seed` & `np.random.seed`
& `random.seed` to create a reproducible sequence of tensors across
multiple calls. (default :obj: `None`, i.e., using random seed)
name: string. optional
Specified name for the model. (default: :str: `class.__name__`)
kwargs: other customized keyword Parameters.
"""
super().__init__(*graph, device=device, seed=seed, name=name, **kwargs)
if not n_clusters:
n_clusters = self.graph.n_classes
self.n_clusters = n_clusters
self.adj_transform = T.get(adj_transform)
self.attr_transform = T.get(attr_transform)
self.process()
def __init__(self,
*graph,
batch_size=256,
rank=100,
adj_transform="normalize_adj",
attr_transform=None,
device='cpu:0',
seed=None,
name=None,
**kwargs):
"""Create a Fast Graph Convolutional Networks (FastGCN) model.
This can be instantiated in several ways:
model = FastGCN(graph)
with a `graphgallery.data.Graph` instance representing
A sparse, attributed, labeled graph.
model = FastGCN(adj_matrix, attr_matrix, labels)
where `adj_matrix` is a 2D Scipy sparse matrix denoting the graph,
`attr_matrix` is a 2D Numpy array-like matrix denoting the node
attributes, `labels` is a 1D Numpy array denoting the node labels.
Parameters:
----------
graph: An instance of `graphgallery.data.Graph` or a tuple (list) of inputs.
A sparse, attributed, labeled graph.
batch_size (Positive integer, optional):
Batch size for the training nodes. (default :int: `256`)
rank (Positive integer, optional):
The selected nodes for each batch nodes, `rank` must be smaller than
`batch_size`. (default :int: `100`)
adj_transform: string, `transform`, or None. optional
How to transform the adjacency matrix. See `graphgallery.transforms`
(default: :obj:`'normalize_adj'` with normalize rate `-0.5`.
i.e., math:: \hat{A} = D^{-\frac{1}{2}} A D^{-\frac{1}{2}})
attr_transform: string, `transform`, or None. optional
How to transform the node attribute matrix. See `graphgallery.transforms`
(default :obj: `None`)
device: string. optional
The device where the model is running on. You can specified `CPU` or `GPU`
for the model. (default: :str: `CPU:0`, i.e., running on the 0-th `CPU`)
seed: interger scalar. optional
Used in combination with `tf.random.set_seed` & `np.random.seed`
& `random.seed` to create a reproducible sequence of tensors across
multiple calls. (default :obj: `None`, i.e., using random seed)
name: string. optional
Specified name for the model. (default: :str: `class.__name__`)
kwargs: other customized keyword Parameters.
"""
super().__init__(*graph, device=device, seed=seed, name=name, **kwargs)
self.rank = rank
self.batch_size = batch_size
self.adj_transform = T.get(adj_transform)
self.attr_transform = T.get(attr_transform)
self.process()
def __init__(self,
*graph,
n_samples=(15, 5),
adj_transform="neighbor_sampler",
attr_transform=None,
device='cpu:0',
seed=None,
name=None,
**kwargs):
"""Create a SAmple and aggreGatE Graph Convolutional Networks (GraphSAGE) model.
This can be instantiated in several ways:
model = GraphSAGE(graph)
with a `graphgallery.data.Graph` instance representing
A sparse, attributed, labeled graph.
model = GraphSAGE(adj_matrix, attr_matrix, labels)
where `adj_matrix` is a 2D Scipy sparse matrix denoting the graph,
`attr_matrix` is a 2D Numpy array-like matrix denoting the node
attributes, `labels` is a 1D Numpy array denoting the node labels.
Parameters:
----------
graph: An instance of `graphgallery.data.Graph` or a tuple (list) of inputs.
A sparse, attributed, labeled graph.
n_samples: List of positive integer. optional
The number of sampled neighbors for each nodes in each layer.
(default :obj: `(15, 5)`, i.e., sample `15` first-order neighbors and
`5` sencond-order neighbors, and the radius for `GraphSAGE` is `2`)
adj_transform: string, `transform`, or None. optional
How to transform the adjacency matrix. See `graphgallery.transforms`
(default: :obj:`'neighbor_sampler'`)
attr_transform: string, `transform`, or None. optional
How to transform the node attribute matrix. See `graphgallery.transforms`
(default :obj: `None`)
device: string. optional
The device where the model is running on. You can specified `CPU` or `GPU`
for the model. (default: :str: `CPU:0`, i.e., running on the 0-th `CPU`)
seed: interger scalar. optional
Used in combination with `tf.random.set_seed` & `np.random.seed`
& `random.seed` to create a reproducible sequence of tensors across
multiple calls. (default :obj: `None`, i.e., using random seed)
name: string. optional
Specified name for the model. (default: :str: `class.__name__`)
kwargs: other customized keyword Parameters.
"""
super().__init__(*graph, device=device, seed=seed, name=name, **kwargs)
self.n_samples = n_samples
self.adj_transform = T.get(adj_transform)
self.attr_transform = T.get(attr_transform)
self.process()
def __init__(self,
*graph,
n_samples=50,
adj_transform="normalize_adj",
attr_transform=None,
device='cpu:0',
seed=None,
name=None,
**kwargs):
"""Create a sample-based Batch Virtual Adversarial Training
Graph Convolutional Networks (SBVAT) model.
This can be instantiated in several ways:
model = SBVAT(graph)
with a `graphgallery.data.Graph` instance representing
A sparse, attributed, labeled graph.
model = SBVAT(adj_matrix, attr_matrix, labels)
where `adj_matrix` is a 2D Scipy sparse matrix denoting the graph,
`attr_matrix` is a 2D Numpy array-like matrix denoting the node
attributes, `labels` is a 1D Numpy array denoting the node labels.
Parameters:
----------
graph: An instance of `graphgallery.data.Graph` or a tuple (list) of inputs.
A sparse, attributed, labeled graph.
n_samples (Positive integer, optional):
The number of sampled subset nodes in the graph where the length of the
shortest path between them is at least `4`. (default :obj: `50`)
adj_transform: string, `transform`, or None. optional
How to transform the adjacency matrix. See `graphgallery.transforms`
(default: :obj:`'normalize_adj'` with normalize rate `-0.5`.
i.e., math:: \hat{A} = D^{-\frac{1}{2}} A D^{-\frac{1}{2}})
attr_transform: string, `transform`, or None. optional
How to transform the node attribute matrix. See `graphgallery.transforms`
(default :obj: `None`)
device: string. optional
The device where the model is running on. You can specified `CPU` or `GPU`
for the model. (default: :str: `CPU:0`, i.e., running on the 0-th `CPU`)
seed: interger scalar. optional
Used in combination with `tf.random.set_seed` & `np.random.seed`
& `random.seed` to create a reproducible sequence of tensors across
multiple calls. (default :obj: `None`, i.e., using random seed)
name: string. optional
Specified name for the model. (default: :str: `class.__name__`)
kwargs: other customized keyword Parameters.
"""
super().__init__(*graph, device=device, seed=seed, name=name, **kwargs)
self.adj_transform = T.get(adj_transform)
self.attr_transform = T.get(attr_transform)
self.n_samples = n_samples
self.process()
def __init__(self,
*graph,
adj_transform="normalize_adj",
attr_transform=None,
device='cpu:0',
seed=None,
name=None,
**kwargs):
"""Create a Edge Convolution version of Graph Convolutional Networks (EdgeGCN) model.
This can be instantiated in several ways:
model = EdgeGCN(graph)
with a `graphgallery.data.Graph` instance representing
A sparse, attributed, labeled graph.
model = EdgeGCN(adj_matrix, attr_matrix, labels)
where `adj_matrix` is a 2D Scipy sparse matrix denoting the graph,
`attr_matrix` is a 2D Numpy array-like matrix denoting the node
attributes, `labels` is a 1D Numpy array denoting the node labels.
Parameters:
----------
graph: An instance of `graphgallery.data.Graph` or a tuple (list) of inputs.
A sparse, attributed, labeled graph.
adj_transform: string, `transform`, or None. optional
How to transform the adjacency matrix. See `graphgallery.transforms`
(default: :obj:`'normalize_adj'` with normalize rate `-0.5`.
i.e., math:: \hat{A} = D^{-\frac{1}{2}} A D^{-\frac{1}{2}})
attr_transform: string, `transform`, or None. optional
How to transform the node attribute matrix. See `graphgallery.transforms`
(default :obj: `None`)
device: string. optional
The device where the model is running on. You can specified `CPU` or `GPU`
for the model. (default: :str: `CPU:0`, i.e., running on the 0-th `CPU`)
seed: interger scalar. optional
Used in combination with `tf.random.set_seed` & `np.random.seed`
& `random.seed` to create a reproducible sequence of tensors across
multiple calls. (default :obj: `None`, i.e., using random seed)
name: string. optional
Specified name for the model. (default: :str: `class.__name__`)
kwargs: other customized keyword Parameters.
Note:
----------
The Graph Edge Convolutional implements the operation using message passing
framework, i.e., using Tensor `edge index` and `edge weight` of adjacency
matrix to aggregate neighbors' message, instead of SparseTensor `adj`.
"""
super().__init__(*graph, device=device, seed=seed, name=name, **kwargs)
self.adj_transform = T.get(adj_transform)
self.attr_transform = T.get(attr_transform)
self.process()
def __init__(self,
*graph,
adj_transform="wavelet_basis",
attr_transform=None,
device='cpu:0',
seed=None,
name=None,
**kwargs):
"""Create a Graph Wavelet Neural Networks (GWNN) model.
This can be instantiated in several ways:
model = GWNN(graph)
with a `graphgallery.data.Graph` instance representing
A sparse, attributed, labeled graph.
model = GWNN(adj_matrix, attr_matrix, labels)
where `adj_matrix` is a 2D Scipy sparse matrix denoting the graph,
`attr_matrix` is a 2D Numpy array-like matrix denoting the node
attributes, `labels` is a 1D Numpy array denoting the node labels.
Parameters:
----------
graph: An instance of `graphgallery.data.Graph` or a tuple (list) of inputs.
A sparse, attributed, labeled graph.
adj_transform: string, `transform`, or None. optional
How to transform the adjacency matrix. See `graphgallery.transforms`
(default: :obj:`'wavelet_basis'`.)
attr_transform: string, `transform`, or None. optional
How to transform the node attribute matrix. See `graphgallery.transforms`
(default :obj: `None`)
device: string. optional
The device where the model is running on. You can specified `CPU` or `GPU`
for the model. (default: :str: `CPU:0`, i.e., running on the 0-th `CPU`)
seed: interger scalar. optional
Used in combination with `tf.random.set_seed` & `np.random.seed`
& `random.seed` to create a reproducible sequence of tensors across
multiple calls. (default :obj: `None`, i.e., using random seed)
name: string. optional
Specified name for the model. (default: :str: `class.__name__`)
kwargs: other customized keyword Parameters.
"""
super().__init__(*graph, device=device, seed=seed, name=name, **kwargs)
self.adj_transform = T.get(adj_transform)
self.attr_transform = T.get(attr_transform)
self.process()
