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Get all edges linked to a given node in a networkx graph

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问题 Just wondering if there is convenient networkx function that returns a list of edges connected to a given node (or nodes) (e.g. my_node_name ) in a graph (e.g. G ). I can do it this way: edlist=[] for ed in G.edges(): if 'my_node_name' in ed: edlist.append(ed) but expect there might be a better way? 回答1: If the graph is undirected, you can use G.edges(node) In networkx 2.x this is an EdgeDataView object. In networkx 1.x this is a list - if you want a generator in 1.x rather than getting the

Get all edges linked to a given node in a networkx graph

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NetworkX系列教程(10)-算法之五:广度优先与深度优先

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小书匠 Graph 图论重头戏部分来了,写到这里我感觉得仔细认真点了,可能在NetworkX中,实现某些算法就一句话的事,但是这个算法是做什么的,用在什么地方,原理是怎么样的,不清除,所以,我决定先把图论中常用算法弄个明白在写这部分. 图论常用算法看我的博客: 下面我将使用NetworkX实现上面的算法,建议不清楚的部分打开两篇博客对照理解. 我将图论的经典问题及常用算法的总结写在下面两篇博客中: 图论---问题篇图论---算法篇目录: 注意: 如果代码出现找不库,请返回第一个教程,把库文件导入. 11.6广度优先搜索算法(BFS) #构建一个长度为10的路径 G = nx.path_graph( 10 ) #显示graph nx.draw_spring(G,with_labels= True ) plt.axis( 'on' ) plt.xticks([]) plt.yticks([]) plt.show() #以4为顶点,广度遍历 print(list(nx.bfs_tree(G, 4 ))) 广度优先搜索算法示例输出: [4, 3, 5, 2, 6, 1, 7, 0, 8, 9] 11.7深度优先搜索算法(DFS) #构建一个长度为10的路径 G = nx.path_graph( 10 ) #显示graph nx.draw_spring(G,with_labels

Spanning tree of directed graph with networkx

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问题 I have a directed graph G in networkx and I want to get the minimum spanning tree of it. I do: T = nx.algorithms.minimum_spanning_tree( G.to_undirected() ) This is undirected and I would like to restore the directions but I don't know how to do it. I tried: G[T.edges()] This last line looks very pythonic but this is not the way networkx works, apparently... Does anyone knows how to do it? In other words: How can I get the subgraph of a directed tree given the (undirected) edges ? 回答1: You can

Spanning tree of directed graph with networkx

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《Python数据分析与机器学习实战-唐宇迪》读书笔记第9章--随机森林项目实战——气温预测(2/2)

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python数据分析个人学习读书笔记-目录索引第9章--随机森林项目实战——气温预测(2/2) 　　第8章已经讲解过随机森林的基本原理，本章将从实战的角度出发，借助Python工具包完成气温预测任务，其中涉及多个模块，主要包含随机森林建模、特征选择、效率对比、参数调优等。这个例子实在太长了，分为3篇介绍。这是第2篇。 9.2数据与特征对结果影响分析　　带着上节提出的问题，重新读取规模更大的数据，任务还是保持不变，需要分别观察数据量和特征的选择对结果的影响。 1 # 导入工具包 2 import pandas as pd 3 4 # 读取数据 5 features = pd.read_csv( ' data/temps_extended.csv ' ) 6 features.head(5 ) 7 8 print( ' 数据规模 ',features.shape) 　　数据规模 (2191, 12) 　　在新的数据中，数据规模发生了变化，数据量扩充到2191条，并且加入了以下3个新的天气特征。 ws_1：前一天的风速。 prcp_1：前一天的降水。 snwd_1：前一天的积雪深度。　　既然有了新的特征，就可绘图进行可视化展示。 1 # 设置整体布局 2 fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2,

How to extract convolutional neural network from Keras model object to Networkx DiGraph object keeping weights as an edge attribute?

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问题 The bounty expires in 4 days . Answers to this question are eligible for a +100 reputation bounty. Galen wants to draw more attention to this question: I'm putting this question up for bounty because it has received little attention despite its potential use for myself and other researchers. I'm willing to work those attempting to answer the question both in terms of clarifying and refining the question as we find issues. I'm interested in using the Networkx Python package to perform network

How to extract convolutional neural network from Keras model object to Networkx DiGraph object keeping weights as an edge attribute?

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How to extract convolutional neural network from Keras model object to Networkx DiGraph object keeping weights as an edge attribute?

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Creating a graph with images as nodes

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问题 I'm creating a graph with nodes as images, # image from http://matplotlib.sourceforge.net/users/image_tutorial.html I want to create a circular layout, with node zero positioned at the center.Egdelist is [(0,1),(0,2),(0,3),(0,4),(0,5)] import matplotlib.pyplot as plt import matplotlib.image as mpimg import numpy as np import networkx as nx img=mpimg.imread('stinkbug.png') G=nx.complete_graph(6) G.node[0]['image']=img G.node[1]['image']=img G.node[2]['image']=img G.node[3]['image']=img G.node

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