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Specifications and Common Mistakes

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mindspore_gl.graph.norm

mindspore_gl.graph.norm(edge_index, num_nodes, edge_weight=None, normalization='sym', lambda_max=None, batch=None)

graph laplacian normalization.

Parameters

• edge_index (Tensor) – Edge index. The shape is $(2,N\mathrm{\_}e)$ where $N\mathrm{\_}e$ is the number of edges.

• num_nodes (int) – Number of nodes.

• edge_weight (Tensor) – Edge weights. The shape is $(N\mathrm{\_}e)$ where $N\mathrm{\_}e$ is the number of edges. Default: None.

• normalization (str) –

  Normalization method. Default: 'sym'. $(L)$ is normalized matrix, $(D)$ is degree matrix, $(A)$ is adjaceny matrix, $(I)$ is unit matrix.

  1. None: No normalization $\mathbf{L}=\mathbf{D}-\mathbf{A}$

  2. 'sym': Symmetric normalization $\mathbf{L}=\mathbf{I}-{\mathbf{D}}^{-1/2}\mathbf{A}{\mathbf{D}}^{-1/2}$

  3. 'rw': Random-walk normalization $\mathbf{L}=\mathbf{I}-{\mathbf{D}}^{-1}\mathbf{A}$

• lambda_max (int, float) – Lambda value of graph. Default: None.

• batch (Tensor) – Batch vector. Default: None.

Returns

• edge_index (Tensor) - normalized edge_index.

• edge_weight (Tensor) - normalized edge_weight

Raises

ValueError – if normalization not is None or 'sym' or 'rw'.

Supported Platforms:

Ascend GPU

Examples

>>> import mindspore as ms
>>> from mindspore_gl.graph import norm
>>> edge_index = [[1, 1, 2, 2], [0, 2, 0, 1]]
>>> edge_index = ms.Tensor(edge_index, ms.int32)
>>> num_nodes = 3
>>> edge_index, edge_weight = norm(edge_index, num_nodes)
>>> print(edge_index)
[[1 1 2 2 0 1 2]
 [0 2 0 1 0 1 2]]
>>> print(edge_weight)
[-0.        -0.4999999 -0.        -0.4999999  1.         1.
  1.       ]