graph

问题 I have a text contains a graph with more than 3 millions nodes and I need to visualize this graph, I've looked for some tools to help me visualizing this graph but they can visualize up to 100K / 1M nodes. Can anyone suggest a way or tool to visualize such a huge graph ?? 回答1: You will have a hard time visualizing all your graph as a node-link diagram. Tools like Gephi or Cytoscape for example will not help you. Furthermore, if you were to visualize such a huge graph, you would most likely

问题 I have a text contains a graph with more than 3 millions nodes and I need to visualize this graph, I've looked for some tools to help me visualizing this graph but they can visualize up to 100K / 1M nodes. Can anyone suggest a way or tool to visualize such a huge graph ?? 回答1: You will have a hard time visualizing all your graph as a node-link diagram. Tools like Gephi or Cytoscape for example will not help you. Furthermore, if you were to visualize such a huge graph, you would most likely

问题 I am trying networkx and visualization in matplotlib an I'm confused becouse I do not clearly understand how do they interact with each other? There simple example import matplotlib.pyplot import networkx as nx G=nx.path_graph(8) nx.draw(G) matplotlib.pyplot.show() Where do I tell pyplot, that I want to draw graph G? I guess that nx.draw use something like matplotlib.pyplot.{plot, etc ...} So, if I want to draw 2 graphs: import matplotlib.pyplot import networkx as nx G=nx.path_graph(8) E=nx

问题 Assume a graph is represented by a n x n dimension adjacency matrix. I know the how to get the shortest path matrix for all pairs. But I wonder is there a way to trace all the shortest paths? Blow is the python code implementation. v = len(graph) for k in range(0,v): for i in range(0,v): for j in range(0,v): if graph[i,j] > graph[i,k] + graph[k,j]: graph[i,j] = graph[i,k] + graph[k,j] 回答1: You have to add to your if statement a new matrix to store path reconstruction data (array p which is

问题 Assume a graph is represented by a n x n dimension adjacency matrix. I know the how to get the shortest path matrix for all pairs. But I wonder is there a way to trace all the shortest paths? Blow is the python code implementation. v = len(graph) for k in range(0,v): for i in range(0,v): for j in range(0,v): if graph[i,j] > graph[i,k] + graph[k,j]: graph[i,j] = graph[i,k] + graph[k,j] 回答1: You have to add to your if statement a new matrix to store path reconstruction data (array p which is

问题 This is the problem: I have n points (p1, p2, p3, .. pn), each of them can connect to any other with a determined cost x. Each point belongs to one of a set of point-types (for example "A" "B" "C" "D"...). The input of the method is the path I want to follow, for example "A-B-C-A-D-B". The output is the shortest path connecting the points of the type I give in input so for example "p1-p4-p32-p83-p43-p12" where p1 is an A-type, p4 a B-type, p32 a C-type, p83 an A-type, p43 a D-type and p12 a B

问题 I'm trying to add standard error bars to my data similar to the ones seen on the box plots near the end of the answer on this question: https://stats.stackexchange.com/questions/8137/how-to-add-horizontal-lines-to-ggplot2-boxplot I am using the PlantGrowth dataset, which looks like this (except 30 rows long in total): weight group 1 4.17 ctrl 2 5.58 ctrl 3 4.81 trt1 4 4.17 trt1 5 6.31 trt2 6 5.12 trt2 I have produced this plot with the following code ggplot(PlantGrowth, aes(group, weight))+

问题 I'm trying to add standard error bars to my data similar to the ones seen on the box plots near the end of the answer on this question: https://stats.stackexchange.com/questions/8137/how-to-add-horizontal-lines-to-ggplot2-boxplot I am using the PlantGrowth dataset, which looks like this (except 30 rows long in total): weight group 1 4.17 ctrl 2 5.58 ctrl 3 4.81 trt1 4 4.17 trt1 5 6.31 trt2 6 5.12 trt2 I have produced this plot with the following code ggplot(PlantGrowth, aes(group, weight))+

问题 I have a graph with n nodes as an adjacency matrix . Is it possible to detect a sink in less than O(n) time? If yes, how? If no, how do we prove it? Sink vertex is a vertex that has incoming edges from other nodes and no outgoing edges. 回答1: Suppose to the contrary that there exists an algorithm that queries fewer than (n-2)/2 edges, and let the adversary answer these queries arbitrarily. By the Pigeonhole Principle, there exist (at least) two nodes v, w that are not an endpoint of any edge

问题 I have a graph with n nodes as an adjacency matrix . Is it possible to detect a sink in less than O(n) time? If yes, how? If no, how do we prove it? Sink vertex is a vertex that has incoming edges from other nodes and no outgoing edges. 回答1: Suppose to the contrary that there exists an algorithm that queries fewer than (n-2)/2 edges, and let the adversary answer these queries arbitrarily. By the Pigeonhole Principle, there exist (at least) two nodes v, w that are not an endpoint of any edge