graphwar.functional¶

`to_sparse_tensor`	Convert edge index to a torch_sparse sparse tensor
`to_dense_adj`	Convert edge index to dense adjacency matrix
`spmm`	Sparse matrix multiplication using `torch_scatter`.
`drop_edge`	DropEdge: Sampling edge using a uniform distribution from the "DropEdge: Towards Deep Graph Convolutional Networks on Node Classification" paper (ICLR'20)
`drop_node`	DropNode: Sampling node using a uniform distribution.
`drop_path`	DropPath: a structured form of `graphwar.functional.drop_edge`.

to_sparse_tensor(edge_index: Tensor, edge_weight: Optional[Tensor] = None, num_nodes: Optional[int] = None, is_sorted: bool = False) → SparseTensor[source]¶

Convert edge index to a torch_sparse sparse tensor

Parameters

edge_index (Tensor) – edge index with shape [2, M]
edge_weight (Optional[Tensor], optional) – edge weight with shape [M], by default None
num_nodes (Optional[int], optional) – the number of nodes in the graph, by default None
is_sorted (bool, optional) – whether the edge_index is sorted, by default False

Returns

the output sparse adjacency matrix denoted as torch_sparse.SparseTensor, with shape [num_nodes, num_nodes]

Return type

SparseTensor

to_dense_adj(edge_index: Tensor, edge_weight: Optional[Tensor] = None, num_nodes: Optional[int] = None, fill_value: float = 1.0) → Tensor[source]¶

Convert edge index to dense adjacency matrix

Parameters

edge_index (Tensor) – edge index with shape [2, M]
edge_weight (Optional[Tensor], optional) – edge weight with shape [M], by default None
num_nodes (Optional[int], optional) – the number of nodes in the graph, by default None
fill_value (float) – filling value for elements in the adjacency matrix where edges existed, by default 1.0

Returns

output dense adjacency matrix with shape [num_nodes, num_nodes]

Return type

Tensor

spmm(x: Tensor, edge_index: Tensor, edge_weight: Optional[Tensor] = None, reduce: str = 'sum') → Tensor[source]¶

Sparse matrix multiplication using torch_scatter.

Parameters

x (Tensor) – the input dense 2D-matrix
edge_index (Tensor) – the location of the non-zeros elements in the sparse matrix, denoted as edge_index with shape [2, M]
edge_weight (Optional[Tensor], optional) – the edge weight of the sparse matrix, by default None
reduce (str, optional) – reduction of the sparse matrix multiplication, by default ‘sum’

Returns

the output result of the multiplication.

Return type

Tensor

Example

>>> import torch
>>> from graphwar.functional import spmm

>>> x = torch.randn(5,2)
>>> edge_index = torch.LongTensor([[1,2], [3,4]])
>>> spmm(x, edge_index)

>>> # which is equivalent to:
>>> A = torch.zeros(5,5)
>>> A[edge_index[0], edge_index[1]] = 1.0
>>> torch.mm(A,x)

drop_edge(edge_index: Tensor, edge_weight: Optional[Tensor] = None, p: float = 0.5, training: bool = True) → Tuple[Tensor, Tensor][source]¶

DropEdge: Sampling edge using a uniform distribution from the “DropEdge: Towards Deep Graph Convolutional Networks on Node Classification” paper (ICLR’20)

Parameters

edge_index (Tensor) – the input edge index
edge_weight (Optional[Tensor], optional) – the input edge weight, by default None
p (float, optional) – the probability of dropping out on each edge, by default 0.5
training (bool, optional) – whether the model is during training, do nothing if training=True,, by default True

Returns

the output edge index and edge weight

Return type

Tuple[Tensor, Tensor]

Raises

ValueError – p is out of range [0,1]

Example

>>> from graphwar.functional import drop_edge
>>> edge_index = torch.LongTensor([[1, 2], [3,4]])
>>> drop_edge(edge_index, p=0.5)

See also

graphwar.nn.layers.DropNode

drop_path(edge_index: Tensor, edge_weight: Optional[Tensor] = None, r: Optional[Union[float, Tensor]] = 0.5, walks_per_node: int = 2, walk_length: int = 4, p: float = 1, q: float = 1, training: bool = True, num_nodes: Optional[int] = None, by: str = 'degree') → Tuple[Tensor, Tensor][source]¶

DropPath: a structured form of graphwar.functional.drop_edge. From the “MaskGAE: Masked Graph Modeling Meets Graph Autoencoders” paper (arXiv’22)

Parameters

edge_index (Tensor) – the input edge index
edge_weight (Optional[Tensor], optional) – the input edge weight, by default None
r (Optional[Union[float, Tensor]], optional) – if r is integer value: the percentage of nodes in the graph that chosen as root nodes to perform random walks, by default 0.5 if r is torch.Tensor: a set of custom root nodes
walks_per_node (int, optional) – number of walks per node, by default 2
walk_length (int, optional) – number of walk length per node, by default 4
p (float, optional) – p in random walks, by default 1
q (float, optional) – q in random walks, by default 1
training (bool, optional) – whether the model is during training, do nothing if training=True, by default True
num_nodes (int, optional) – number of total nodes in the graph, by default None
by (str, optional) – sampling root nodes uniformly uniform or by degree distribution degree, by default ‘degree’

Returns

the output edge index and edge weight

Return type

Tuple[Tensor, Tensor]

Raises

ImportError – if torch_cluster is not installed.
ValueError – r is out of scope [0,1]
ValueError – r is not integer value or a Tensor

Example

>>> from graphwar.functional import drop_path
>>> edge_index = torch.LongTensor([[1, 2], [3,4]])
>>> drop_path(edge_index, r=0.5)

>>> drop_path(edge_index, r=torch.tensor([1,2])) # specify root nodes