graph_reindex

paddle.incubate. graph_reindex ( x, neighbors, count, value_buffer=None, index_buffer=None, flag_buffer_hashtable=False, name=None ) [source]

Warning

API “paddle.incubate.operators.graph_reindex.graph_reindex” is deprecated since 2.4.0, and will be removed in future versions. Please use “paddle.geometric.reindex_graph” instead. Reason: paddle.incubate.graph_reindex will be removed in future

Graph Reindex API.

This API is mainly used in Graph Learning domain, which should be used in conjunction with graph_sample_neighbors API. And the main purpose is to reindex the ids information of the input nodes, and return the corresponding graph edges after reindex.

Notes

The number in x should be unique, otherwise it would cause potential errors. Besides, we also support multi-edge-types neighbors reindexing. If we have different edge_type neighbors for x, we should concatenate all the neighbors and count of x. We will reindex all the nodes from 0.

Take input nodes x = [0, 1, 2] as an example. If we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2], then we know that the neighbors of 0 is [8, 9], the neighbors of 1 is [0, 4, 7], and the neighbors of 2 is [6, 7].

Parameters

  • x (Tensor) – The input nodes which we sample neighbors for. The available data type is int32, int64.

  • neighbors (Tensor) – The neighbors of the input nodes x. The data type should be the same with x.

  • count (Tensor) – The neighbor count of the input nodes x. And the data type should be int32.

  • value_buffer (Tensor, optional) – Value buffer for hashtable. The data type should be int32, and should be filled with -1. Default is None.

  • index_buffer (Tensor, optional) – Index buffer for hashtable. The data type should be int32, and should be filled with -1. Default is None.

  • flag_buffer_hashtable (bool, optional) – Whether to use buffer for hashtable to speed up. Default is False. Only useful for gpu version currently.

  • name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

  • reindex_src (Tensor), The source node index of graph edges after reindex.

  • reindex_dst (Tensor), The destination node index of graph edges after reindex.

  • out_nodes (Tensor), The index of unique input nodes and neighbors before reindex, where we put the input nodes x in the front, and put neighbor nodes in the back.

Examples

>>> import paddle

>>> x = [0, 1, 2]
>>> neighbors_e1 = [8, 9, 0, 4, 7, 6, 7]
>>> count_e1 = [2, 3, 2]
>>> x = paddle.to_tensor(x, dtype="int64")
>>> neighbors_e1 = paddle.to_tensor(neighbors_e1, dtype="int64")
>>> count_e1 = paddle.to_tensor(count_e1, dtype="int32")

>>> reindex_src, reindex_dst, out_nodes = paddle.incubate.graph_reindex(
...     x,
...     neighbors_e1,
...     count_e1,
... )
>>> print(reindex_src)
Tensor(shape=[7], dtype=int64, place=Place(cpu), stop_gradient=True,
[3, 4, 0, 5, 6, 7, 6])
>>> print(reindex_dst)
Tensor(shape=[7], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 0, 1, 1, 1, 2, 2])
>>> print(out_nodes)
Tensor(shape=[8], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 1, 2, 8, 9, 4, 7, 6])

>>> neighbors_e2 = [0, 2, 3, 5, 1]
>>> count_e2 = [1, 3, 1]
>>> neighbors_e2 = paddle.to_tensor(neighbors_e2, dtype="int64")
>>> count_e2 = paddle.to_tensor(count_e2, dtype="int32")

>>> neighbors = paddle.concat([neighbors_e1, neighbors_e2])
>>> count = paddle.concat([count_e1, count_e2])
>>> reindex_src, reindex_dst, out_nodes = paddle.incubate.graph_reindex(x, neighbors, count)
>>> print(reindex_src)
Tensor(shape=[12], dtype=int64, place=Place(cpu), stop_gradient=True,
[3, 4, 0, 5, 6, 7, 6, 0, 2, 8, 9, 1])
>>> print(reindex_dst)
Tensor(shape=[12], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2])
>>> print(out_nodes)
Tensor(shape=[10], dtype=int64, place=Place(cpu), stop_gradient=True,
[0, 1, 2, 8, 9, 4, 7, 6, 3, 5])