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):

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

%% 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

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

function A = build_adjacency_from_list(ij)

% function A = build_adjacency_from_list(ij)
%
% ij: row, column

A = zeros(max(max(ij)),max(max(ij)));

for i = 1:length(ij)
    A(ij(i,1), ij(i,2)) = 1;

wanirepo

function q = modularity_wani(A,z,varargin)

% function q = modularity_wani(A,z, [optional 'scalar' or 'degree'])
%
% feature: This function calculates the modularity (same with 
%          assortativity), q. It works for categorical and continuous
%          (needs optional input, 'scalar') attributes. 
% 
% input:   z   attributes or category membership
%          A   adjacency matrix

wanirepo

function [max_z, max_q, outinfo] = community_modularity(A, varargin)

% function [z, q, outinfo] = community_modularity(A, optional_inputs)
%
% feature: This function conduct the greedy agglomerative algorithm to find
%          community structure that maximizes the network's modularity (Q). 
%
% input:   A      adjacency matrix
%
% output:  max_z    a vector of community membership that maximize Q

wanirepo

function nmi = norm_mutual_info(za, zb)

% function nmi = norm_mutual_info(za, zb)
%
% feature: This function calculates the normalized mutual information,
%          which tells us how much information we learn about Community 
%          structure A (or B) if we know B (or A). If A and B are 
%          identical, we learn everything about A from B (or B from A). 
%          In this case, nmi returns 1. If they are entirely uncorrelated, 
%          we learn nothing. In this case, nmi returns 0. 

wanirepo

% Data info: 
%   Each of the school .mat files has an A matrix (sparse) and a
%   "local_info" variable, one row per node: a student/faculty status
%   flag, gender, major, second major/minor (if applicable), dorm/house,
%   year, and high school. Missing data is coded 0.

% datdir = '/Users/clinpsywoo/Documents/2011-2016yr/2014_2015_4th_GS/Network_modeling/Problem_sets/facebook100';
datdir = '/Volumes/engram/Users/wani/Documents/Network_modeling/Problem_sets/facebook100';
fnames = filenames(fullfile(datdir, '*mat'));