Invalid MEX-file error when using betweenness_centrality.m script - not sure why!

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Emre 2023-5-31

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https://ww2.mathworks.cn/matlabcentral/answers/1976134-invalid-mex-file-error-when-using-betweenness_centrality-m-script-not-sure-why

评论： Emre 2023-5-31

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Hey all,

I am trying to run the 'betweenness_centrality.m' script from the MatlabBGL package.

I am using a 64-bit macbook air and am using the matlab version 'Matlab2023a'.

However, when I try to run it, I get the following error:

Invalid MEX-file '/Users/macbook/Desktop/UCL PhD
Work/py_scripts/betweenness_centrality_mex.mexmaci64':
dlopen(/Users/macbook/Desktop/UCL PhD
Work/py_scripts/betweenness_centrality_mex.mexmaci64, 0x0006): Library not loaded:
'@loader_path/libmex.dylib'
  Referenced from: '/Users/macbook/Desktop/UCL PhD
  Work/py_scripts/betweenness_centrality_mex.mexmaci64'
  Reason: tried: '/Users/macbook/Desktop/UCL PhD Work/py_scripts/libmex.dylib' (no
  such file), '/usr/local/lib/libmex.dylib' (no such file),
  '/usr/lib/libmex.dylib' (no such file)
Error in betweenness_centrality (line 110)
    bc = betweenness_centrality_mex(A,weight_arg);
Error in Spacesocialproject (line 3)
bc = betweenness_centrality(G);

I have ensured that the 'betweenness_centrality_mex.mexmaci64' file is in the same directory as the matlab scripts.

I would be so grateful for a helping hand as to why this may be!

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Emre 2023-5-31

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Thank you so so much!

When I try the following, I get the same betweenness centrality values as on python (bcn variable):

import matlab.graph.*; 
G = graph(G); 
bc = centrality(G,'betweenness');

However, when I try calculting closeness centrality, the outputs are different between python and matlab.

Why might this be?

Matlab code:

import matlab.graph.*; 
G = graph(G); 
cc = centrality(G,'closeness');

Python code:

import networkx as nx
import pandas as pd 
newframe2 = pd.read_excel(r'/Users/macbook/Desktop/UCL PhD Work/py_scripts/Betweennesscentralitymatrix.xlsx',header=None)
graph = nx.from_numpy_matrix(newframe2.values)
closeness = nx.closeness_centrality(graph)
df_closeness = pd.DataFrame(columns=['Node', 'Closeness Centrality'])
for node, centrality in closeness.items():
    df_closeness = df_closeness.append({'Node': node, 'Closeness Centrality': centrality}, ignore_index=True)
df_closeness['Node'] = df_closeness['Node'].astype(int)
df_closeness.to_csv(r'/Users/macbook/Desktop/UCL PhD Work/py_scripts/Closenesscentralitymatrixfinal.xlsx')
df_closeness['Closeness Centrality'] = df_closeness['Closeness Centrality'].round(6)
df_closeness.to_csv(r'/Users/macbook/Desktop/UCL PhD Work/py_scripts/Closenesscentralitymatrixfinal_rounded.csv', index=False)

Steven Lord 2023-5-31

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Looks like they differ by a scaling factor.

format longg
P = [0.28888888888888886, 0.28888888888888886, 0.28888888888888886, 0.25742574257425743];
M = [0.0111, 0.0111, 0.0111, 0.0099];
scaleFactor = P./M
scaleFactor = 1×4
           26.026026026026           26.026026026026           26.026026026026           26.002600260026

I'm betting if you actually used all the decimal places stored in the values in MATLAB each element of the scaling factor vector would be even closer to the same number.

I don't know what scaling factor or method Python uses. If you forced me to guess perhaps their definition doesn't divide by