numpy/scipy 从加权边列表构建邻接矩阵 [英] numpy/scipy build adjacency matrix from weighted edgelist

问题描述

我正在读取一个加权的 egdelist/numpy 数组，例如:

I'm reading a weighted egdelist / numpy array like:

0 1 1
0 2 1
1 2 1
1 0 1
2 1 4

其中的列是User1"、User2"、Weight".我想用 scipy.sparse.csgraph.depth_first_tree 执行 DFS 算法，它需要一个 N x N 矩阵作为输入.如何将上一个列表转换为方阵:

where the columns are 'User1','User2','Weight'. I'd like to perform a DFS algorithm with scipy.sparse.csgraph.depth_first_tree, which requires a N x N matrix as input. How can I convert the previous list into a square matrix as:

0 1 1
1 0 1
0 4 0

在 numpy 或 scipy 中?

within numpy or scipy?

感谢您的帮助.

我一直在使用一个巨大的(1.5 亿个节点)网络，所以我正在寻找一种内存高效的方法来做到这一点.

I've been working with a huge (150 million nodes) network, so I'm looking for a memory efficient way to do that.

推荐答案

您可以使用内存高效的 scipy.sparse 矩阵:

You could use a memory-efficient scipy.sparse matrix:

import numpy as np
import scipy.sparse as sparse

arr = np.array([[0, 1, 1],
                [0, 2, 1],
                [1, 2, 1],
                [1, 0, 1],
                [2, 1, 4]])
shape = tuple(arr.max(axis=0)[:2]+1)
coo = sparse.coo_matrix((arr[:, 2], (arr[:, 0], arr[:, 1])), shape=shape,
                        dtype=arr.dtype)

print(repr(coo))
# <3x3 sparse matrix of type '<type 'numpy.int64'>'
#   with 5 stored elements in COOrdinate format>

要将稀疏矩阵转换为密集的 numpy 数组，可以使用 todense:

To convert the sparse matrix to a dense numpy array, you could use todense:

print(coo.todense())
# [[0 1 1]
#  [1 0 1]
#  [0 4 0]]

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