Efficiently create adjacency matrix from network graph (vice versa) Python NetworkX

Question

I\'m trying to get into creating network graphs and generating sparse matrices from them. From the wikipedia Laplacian matrix example, I decided to try and recreat

Answer 1

I was confronted with the same problem, and found a solution. We can use the function from_numpy_matrix, which is depicted in official website http://networkx.github.io/documentation/networkx-1.7/reference/generated/networkx.convert.from_numpy_matrix.html. Pay attention that the input data usually needs to be modified by numpy.matrix(). The example given is that:

    import numpy
    A=numpy.matrix([[1,1],[2,1]])
    G=nx.from_numpy_matrix(A)

It's really useful.

Answer 2

How to convert from graph to adjacency matrix:

import scipy as sp
import networkx as nx
G=nx.fast_gnp_random_graph(100,0.04)
adj_matrix = nx.adjacency_matrix(G)

Here's the documentation.

And from adjacency matrix to graph:

H=nx.Graph(adj_matrix)  #if it's directed, use H=nx.DiGraph(adj_matrix)

Here's the documentation.