Plotting a network using a co-occurrence matrix
I want to plot a network in Python using a co-occurence matrix as an input, such that nodes that have a non-zero co-occurence count are connected, and the weight of the edges is proportional to the number of co-occurrences between each node.
Is there a python library in existence that will facilitate this task using a co-occurence matrix as an input?
python plot network-analysis
asked Nov 23 '18 at 10:15
RDG
6816
add a comment |
I want to plot a network in Python using a co-occurence matrix as an input, such that nodes that have a non-zero co-occurence count are connected, and the weight of the edges is proportional to the number of co-occurrences between each node.
Is there a python library in existence that will facilitate this task using a co-occurence matrix as an input?
python plot network-analysis
asked Nov 23 '18 at 10:15
RDG
6816
add a comment |
I want to plot a network in Python using a co-occurence matrix as an input, such that nodes that have a non-zero co-occurence count are connected, and the weight of the edges is proportional to the number of co-occurrences between each node.
Is there a python library in existence that will facilitate this task using a co-occurence matrix as an input?
python plot network-analysis
asked Nov 23 '18 at 10:15
RDG
6816
I want to plot a network in Python using a co-occurence matrix as an input, such that nodes that have a non-zero co-occurence count are connected, and the weight of the edges is proportional to the number of co-occurrences between each node.
Is there a python library in existence that will facilitate this task using a co-occurence matrix as an input?
python plot network-analysis
python plot network-analysis
asked Nov 23 '18 at 10:15
RDG
6816
asked Nov 23 '18 at 10:15
RDG
6816
asked Nov 23 '18 at 10:15
RDG
6816
asked Nov 23 '18 at 10:15
RDG
6816
asked Nov 23 '18 at 10:15
RDG
6816
6816
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
You might find NetworkX to be a useful tool for that. You can easily feed it the input nodes and edges in several ways.
In the case that you want to generate your network using a co-occurrence matrix, you can use NetworkX's method from_numpy_matrix, which allows you to create a graph from a numpy matrix matrix which will be interpreted as an adjacency matrix.
Here's a simply toy example from the documentation:
import numpy as np
import networkx as nx
A=np.matrix([[1,1],[2,1]])
G=nx.from_numpy_matrix(A)
answered Nov 23 '18 at 10:31
yatu
5,2511423
add a comment |
It is indeed possible to do something like that with networkx
Check this: https://stackoverflow.com/a/25651827/4288795
With it you can generate graphs like this:
answered Nov 23 '18 at 10:52
Pedro Torres
683413
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
You might find NetworkX to be a useful tool for that. You can easily feed it the input nodes and edges in several ways.
In the case that you want to generate your network using a co-occurrence matrix, you can use NetworkX's method from_numpy_matrix, which allows you to create a graph from a numpy matrix matrix which will be interpreted as an adjacency matrix.
Here's a simply toy example from the documentation:
import numpy as np
import networkx as nx
A=np.matrix([[1,1],[2,1]])
G=nx.from_numpy_matrix(A)
answered Nov 23 '18 at 10:31
yatu
5,2511423
add a comment |
You might find NetworkX to be a useful tool for that. You can easily feed it the input nodes and edges in several ways.
In the case that you want to generate your network using a co-occurrence matrix, you can use NetworkX's method from_numpy_matrix, which allows you to create a graph from a numpy matrix matrix which will be interpreted as an adjacency matrix.
Here's a simply toy example from the documentation:
import numpy as np
import networkx as nx
A=np.matrix([[1,1],[2,1]])
G=nx.from_numpy_matrix(A)
answered Nov 23 '18 at 10:31
yatu
5,2511423
add a comment |
You might find NetworkX to be a useful tool for that. You can easily feed it the input nodes and edges in several ways.
In the case that you want to generate your network using a co-occurrence matrix, you can use NetworkX's method from_numpy_matrix, which allows you to create a graph from a numpy matrix matrix which will be interpreted as an adjacency matrix.
Here's a simply toy example from the documentation:
import numpy as np
import networkx as nx
A=np.matrix([[1,1],[2,1]])
G=nx.from_numpy_matrix(A)
answered Nov 23 '18 at 10:31
yatu
5,2511423
You might find NetworkX to be a useful tool for that. You can easily feed it the input nodes and edges in several ways.
In the case that you want to generate your network using a co-occurrence matrix, you can use NetworkX's method from_numpy_matrix, which allows you to create a graph from a numpy matrix matrix which will be interpreted as an adjacency matrix.
Here's a simply toy example from the documentation:
import numpy as np
import networkx as nx
A=np.matrix([[1,1],[2,1]])
G=nx.from_numpy_matrix(A)
answered Nov 23 '18 at 10:31
yatu
5,2511423
edited Nov 23 '18 at 10:38
answered Nov 23 '18 at 10:31
yatu
5,2511423
answered Nov 23 '18 at 10:31
yatu
5,2511423
answered Nov 23 '18 at 10:31
yatu
5,2511423
5,2511423
add a comment |
add a comment |
It is indeed possible to do something like that with networkx
Check this: https://stackoverflow.com/a/25651827/4288795
With it you can generate graphs like this:
answered Nov 23 '18 at 10:52
Pedro Torres
683413
add a comment |
It is indeed possible to do something like that with networkx
Check this: https://stackoverflow.com/a/25651827/4288795
With it you can generate graphs like this:
answered Nov 23 '18 at 10:52
Pedro Torres
683413
add a comment |
It is indeed possible to do something like that with networkx
Check this: https://stackoverflow.com/a/25651827/4288795
With it you can generate graphs like this:
answered Nov 23 '18 at 10:52
Pedro Torres
683413
It is indeed possible to do something like that with networkx
Check this: https://stackoverflow.com/a/25651827/4288795
With it you can generate graphs like this:
answered Nov 23 '18 at 10:52
Pedro Torres
683413
answered Nov 23 '18 at 10:52
Pedro Torres
683413
answered Nov 23 '18 at 10:52
Pedro Torres
683413
answered Nov 23 '18 at 10:52
Pedro Torres
683413
683413
add a comment |
add a comment |
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