adjacency-matrix

问题 Assume we have a digraph, it is not a complete graph and has more than one SCC. I wonder if the patterns of Strongly Connected Component changes if we transpose the graph and use Kosaraju's Algorithm? By saying "transpose the graph" I mean flip the direction of edges. If we try to find SCC in the transposed/reversed graph instead of the original, will the SCC we find be different? I came up with this question as I misunderstood the algorithm of SCC and runs it on my transposed/reversed graph.

问题 I was wondering on how you can get adjacent item within grid view layout? Currently working on function that can determine the adjacent items from the position. I'm subtracting the position minus the columns and it obviously gets more complicated when I'm on the sides and corners. It might be to much but the only option i can think of now, is there easier way? I can get postions from an touch event and the matrix looks like this with postions. 1 2 3 4 5 6 7 8 9 10 11 12 Answer from below

问题 I'm new to this site. I was wondering if anyone had experience with turning a list of grid coordinates (shown in example code below as df). I've written a function that can handle the job for very small data sets but the run time increases exponentially as the size of the data set increases (I think 800 pixels would take about 25 hours). It's because of the nested for loops but I don't know how to get around it. ## Dummy Data x <- c(1,1,2,2,2,3,3) y <- c(3,4,2,3,4,1,2) df <- as.data.frame

问题 I have an adjacency matrix from the package 'bnlearn' using the function amat (the matrix is acyclical). For example: +---+-------------------------------+ | | 1 2 3 4 5 | +---+-------------------------------+ | 1 | 0 1 0 0 0 | | 2 | 0 0 1 0 0 | | 3 | 0 0 0 1 0 | | 4 | 0 0 0 0 1 | | 5 | 0 0 0 0 0 | +---+-------------------------------+ I need to find the complete dependency matrix from this. For one lag dependency matrix I can use: New_matrix<- if(old_matrix+old_matrix*old_matrix)>0 then 1

问题 I have a GML file of a directed graph (Political blogs). I want to use this graph in Matlab as an adjacency matrix. How can I convert it? Thanks. 回答1: There is a sample code here for this purpose: %Extracting edges from gml file graph fileName = 'dolphins.gml'; inputfile = fopen(fileName); A=[]; l=0; k=1; while 1 % Get a line from the input file tline = fgetl(inputfile); % Quit if end of file if ~ischar(tline) break end nums = regexp(tline,'\d+','match'); if length(nums) if l==1 l=0; A(k,2)

问题 I want to write a script to create an image from a connection matrix. Basically, wherever there is a '1' in the matrix, I want that area to be shaded in the image. For eg - I created this image using Photoshop. But I have a large dataset so I will have to automate the process. It would be really helpful if anyone could point me in the right direction. EDIT The image that I am getting after using the script is this. This is due to the fact that the matrix is large (19 x 19). Is there any way I

问题 I have an adjacency matrix like: [[ 0., 15., 0., 7., 10., 0.], [ 15., 0., 9., 11., 0., 9.], [ 0., 9., 0., 0., 12., 7.], [ 7., 11., 0., 0., 8., 14.], [ 10., 0., 12., 8., 0., 8.], [ 0., 9., 7., 14., 8., 0.]] How can I convert it to an adjacency list like this one down here? graph = {'1': [{'2':'15'}, {'4':'7'}, {'5':'10'}], '2': [{'3':'9'}, {'4':'11'}, {'6':'9'}], '3': [{'5':'12'}, {'6':'7'}], '4': [{'5':'8'}, {'6':'14'}], '5': [{'6':'8'}]} ? 回答1: Keep a list of already added edges in a set

问题 I have a problem making this task: An n-vertex graph is a scorpion if it has a vertex 1(the sting) connected to a vertex two (the tail) connected a vertex 3 (the body) connected to the other vertexes (the feet). Some of the feet may be connected to other feet. Design an algorithm that decides whether a given drawing represents a scorpion and tell in which line is sting,tail , body and feets. This is my data file to read from: (+) is where is edge and (-) where are no edges I'm trying to find

问题 I'm trying to find an elegant algorithm for creating an N x N matrix of 1's and 0's, under the restrictions: each row and each column must sum to Q (to be picked freely) the diagonal must be 0's the matrix must be symmetrical. It is not strictly necessary for the matrix to be random (both random and non-random solutions are interesting, however), so for Q even, simply making each row a circular shift of the vector [0 1 1 0 ... 0 0 0 ... 0 1 1] (for Q=4) is a valid solution. However, how to do

问题 I'm preparing for a coding interview, and was refreshing my mind on graphs. I was wondering the following : in all places I've seen, it is assumed that adjacency lists are more memory efficient than adjacency matrices for large sparse graphs, and should thus be preferred in that case. In addition, computing the number of outgoing edges from a node requires O(N) in a matrix while it's O(1) in a list, as well as which are the adjacent nodes in O(num adjacent nodes) for the list instead of O(N)