networkx

问题 I'm stuck trying to solve a problem that I encounter. As mentioned in this post the nx.Graph() function can take a dictionary as an initialising argument. Which works fine, but I had something different in mind. My dictionary looks as follows (contents simplified): graph = {'A': ['a','b','c'], 'B':['a','b','c']} This creates the following: plot As can be seen for 'B', 'A', 'a', 'b' and 'c' nodes have been created. What I am looking for is a way to initialise the keys in my dictionary as nodes

问题 I've managed to create a random undirected weighted graph for testing with Dijkstra's algorithm, but how can I make it so each node has at least one edge that connects them to the graph? I'm using Networkx and my graph generator is as follows: import networkx as nx import random random.seed() nodes = random.randint(5,10) seed = random.randint(1,10) probability = random.random() G = nx.gnp_random_graph(nodes,probability,seed, False) for (u, v) in G.edges(): G.edges[u,v]['weight'] = random

问题 I want to color nodes and edges of Karate club graph. But some of nodes have more than one color. Is there any way to color a node with more than one color in python (especially with networkx)? I need something like this: 回答1: This can be done but it will probably require a lot of work to obtain the exact result you want. You could start with networkx and pygraphviz like this: import networkx as nx karate = nx.generators.social.karate_club_graph() karate_agr = nx.nx_agraph.to_agraph(karate)

问题 I have list of list that looks like this my_list = [[1, 2, 3, 4], [4, 5, 6, 7], [9, 10, 11, 12]] and I would like to find what's the best way to split the list into two groups so that the individual elements in each group are not overlapping. For instance, in the example above the two groups would be group1 = [[1, 2, 3, 4], [4, 5, 6, 7]] group2 = [[9, 10, 11, 12]] and this is because 9, 10, 11, 12 never appear in any of the items of group1 . 回答1: Similarly to Combine lists with common

问题 In MultiGraph, an edge is keyed by (u, v, key) , for instance, ('n1', 'n2', 'key1') . I would like to draw edge labels (say weight, (u, v, key): 10 ) for MultiGraph by using draw_networkx_edge_labels . However, edge labels are keyed by a two-tuple (u, v) in draw_networkx_edge_labels , instead of 3-tuple (u,v,key) in MultiGraph, causing ValueError: too many values to unpack . PS: The parameter edge_labels in draw_networkx_edge_labels is described as follows: draw_networkx_edge_labels(G, pos,

问题 In MultiGraph, an edge is keyed by (u, v, key) , for instance, ('n1', 'n2', 'key1') . I would like to draw edge labels (say weight, (u, v, key): 10 ) for MultiGraph by using draw_networkx_edge_labels . However, edge labels are keyed by a two-tuple (u, v) in draw_networkx_edge_labels , instead of 3-tuple (u,v,key) in MultiGraph, causing ValueError: too many values to unpack . PS: The parameter edge_labels in draw_networkx_edge_labels is described as follows: draw_networkx_edge_labels(G, pos,

问题 I am working on maze solving using Image Processing and NetworkX search algroithm and need to find the shortest connecting path between two points on those lines. #Solving Maze Using Image Processing and NetWorkx search #Open Maze image img = cv2.imread("C:/Users/Dell/HandMadeMaze1.jpg") kernel = np.ones((1,1),np.uint8) #Convert to GrayScaledImage grayscaled = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #BınaryThreshold + OtsuThreshold + BinaryThreshold retval, threshold = cv2.threshold(grayscaled,

问题 I am using python networkx lib draw a node relation graph. Code like this: import networkx as nx import matplotlib.pyplot as plt G = nx.Graph() G.add_edges_from([("leg", 'body'),('body', 'head'),('body','arm'),('arm','hand')]) pos = nx.spring_layout(G) nx.draw_networkx_nodes(G,pos) nx.draw_networkx_edges(G,pos) nx.draw_networkx_labels(G,pos) plt.show() Everything is fine. The figure is: However, I'd like to put the label outside of the node. Then I adjust the position of labels. code is:

问题 Edit: I have written a more succinct version of this question here but I am keeping this post because it is a full explanation. Given a 3D numpy array, marching cubes can form a 3D object around some threshold. import numpy as np from skimage import measure A = np.zeros((12,12,12)) #A[A<1] = -1 for i in np.arange(1,2): for j in np.arange(1,2): for k in np.arange(1,2): A[i,j,k] = 10 for i in np.arange(8,9): for j in np.arange(8,9): for k in np.arange(8,9): A[i,j,k] = 10 verts, faces, normals,

问题 First, I use NetworkX to represent graphs in python. Second, this is the predefined Betweenness centrality in NetworkX. The problem is, we defined the Betweenness Centrality without the denominator. So, c_B(v) is just the sum over all shortes paths they go through the vertex v. Is there a way to modify the predefined Betweenness centrality, such that I get "our" definition of Betweenness centrality? I tried already the load_centrality, but this does not fit too. Best regards, Matthias 回答1: I