使用共现矩阵绘制网络

问题描述

我想使用共现矩阵作为输入在 Python 中绘制一个网络，以便连接具有非零共现计数的节点，并且边的权重与 co 的数量成正比- 每个节点之间的出现次数.

I want to plot a network in Python using a co-occurence matrix as an input, such that nodes that have a non-zero co-occurence count are connected, and the weight of the edges is proportional to the number of co-occurrences between each node.

是否有一个 Python 库可以使用共现矩阵作为输入来促进这项任务?

Is there a python library in existence that will facilitate this task using a co-occurence matrix as an input?

推荐答案

您可能会发现 NetworkX一个有用的工具.您可以通过多种方式轻松地为它提供输入节点和边.

You might find NetworkX to be a useful tool for that. You can easily feed it the input nodes and edges in several ways.

如果你想使用共现矩阵生成你的网络，你可以使用NetworkX的方法from_numpy_matrix，它允许您从 numpy 矩阵矩阵创建一个图形，该矩阵将被解释为邻接矩阵.

In the case that you want to generate your network using a co-occurrence matrix, you can use NetworkX's method from_numpy_matrix, which allows you to create a graph from a numpy matrix matrix which will be interpreted as an adjacency matrix.

这是文档中的一个简单的玩具示例:

Here's a simply toy example from the documentation:

import numpy as np
import networkx as nx

A=np.matrix([[1,1],[2,1]])
G=nx.from_numpy_matrix(A)

这篇关于使用共现矩阵绘制网络的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！