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);

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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;

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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'));

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function L = SBM_likelihood(A, z, varargin)

% usage: L = SBM_likelihood(A, z)
%
% feature: log-likelihood estimation for simple Stochastic Block Model (SBM)
%          (undirected, unweighted) based on Maximum Likelihood estimation. 
%
% input:   A      adjacency matrix (simple graph)
%          z      group membership
%

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function [bestL, bestP, info] = KL_heuristic_k2(A, varargin)

% usage: [bestL, bestP, info] = KL_heuristic_k2(A, varargin)
%
% feature: use the Kernighan-Lin (KL) heuristic to optimize any partition 
%          score function, e.g., modularity Q or stochastic block model's
%          likelihood function. This works only for 2 partitioning problem.
%
% input:   A        adjacency matrix
%