# Get ting the index of the non zero connections in scipy csr graph

A sparse matrix is a matrix in which most elements are zeroes, This is in contrast to a dense matrix, the differentiating characteristic of which you can likely figure out at this point without any help

```
import numpy as np
from scipy
import sparse
X = np.random.uniform(size = (6, 6))
print(X)
```

adjacency – Adjacency matrix of the graph,,adjacency – Adjacency matrix of the graph (symmetric)

```
>>> from sknetwork.topology
import CoreDecomposition
>>>
from sknetwork.data
import karate_club
>>>
kcore = CoreDecomposition() >>>
adjacency = karate_club() >>>
kcore.fit(adjacency) >>>
kcore.core_value_
4
```

Perform a shortest-path graph search on a positive directed or undirected graph,,dijkstra(csgraph[, directed, indices, …]),Dijkstra algorithm using Fibonacci Heaps,johnson(csgraph[, directed, indices, …])

G (0) / \ 1 2 / \ (2)(1)

skan,csr: CSR Graph representation of skeletons,M (scipy

>>> image = np.array([ [1, 0, 1, 0, 0, 1, 1], ...[1, 0, 0, 1, 0, 0, 0] ]) >>> labels, centroids = compute_centroids(image) >>> print(labels)[[1 0 2 0 0 3 3] [1 0 0 2 0 0 0]] >>> centroids array([ [0.5, 0.], [0.5, 2.5], [0., 5.5] ])

CSR (and also CSC, a,k

```
import numpy as npfrom scipy
import sparsefrom sys
import getsizeof # Matrix 1: Create a dense matrix(stored as a full matrix).A_full = np.random.rand(600, 600) # Matrix 2: Store A_full as a sparse matrix(though it is dense).A_sparse = sparse.csc_matrix(A_full) # Matrix 3: Create a sparse matrix(stored as a full matrix).B_full = np.diag(np.random.rand(600)) # Matrix 4: Store B_full as a sparse matrix.B_sparse = sparse.csc_matrix(B_full) # Create a square
function to
return the square of the matrixdef square(A): return np.power(A, 2)
```

A symmetric sparse matrix arises as the adjacency matrix of an undirected graph; it can be stored efficiently as an adjacency list, ,Trilinos, a large C++ library, with sub-libraries dedicated to the storage of dense and sparse matrices and solution of corresponding linear systems

V = [5 8 3 6] COL_INDEX = [0 1 2 1] ROW_INDEX = [0 1 2 3 4]

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