wanirepo

function cent = centrality_wani(A, varargin)

% function cent = centrality_wani(A, varargin)
%
% feature: This function calculate four different centrality measures, 
%          including degree, eigenvector, harmonics, and betweenness 
%          centrality measures. This only works for undirected/unweighted 
%          graph for now. 
%
% input:   A      adjacency matrix

wanirepo

%% make a mask for MPFC

cl = region(which('atlas_labels_combined.img'), 'unique_mask_values'); %anat_lbpa_thal.img'); %% 'lpba40.spm5.avg152T1.label.nii');

cluster_orthviews(cl, 'unique');

clout = [];

% choose MPFC clusters
[clout,cl] = cluster_graphic_select(cl,clout);

wanirepo

datdir = '/Users/clinpsywoo/Documents/2011-2016yr/2014-2015(4th_GS)/Network_modeling/Problem_sets/FB100 dataset/facebook100';
datfiles = filenames(fullfile(datdir, '*mat'), 'absolute');

for i = 1:numel(datfiles)
    
    fprintf('\nWorking on %02d/%02d...\t', i, numel(datfiles)); 
    tic; 
    
    load(datfiles{i});
    

wanirepo

datdir = '/Users/clinpsywoo/Documents/2011-2016yr/2014-2015(4th_GS)/Network_modeling/Problem_sets/FB100 dataset/facebook100';
datfiles = filenames(fullfile(datdir, '*mat'), 'absolute');

for i = 1:numel(datfiles)
    
    fprintf('\nWorking on %02d/%02d...\t', i, numel(datfiles)); 
    tic; 
    
    load(datfiles{i});
    

wanirepo

from os import listdir
from os.path import isfile, join
import multiprocessing
import networkx as nx
from sklearn.utils.graph import single_source_shortest_path_length as sssp


fb_folder = "just8"

def func_sssp(component):