main file and graph plot function, matlab

function [G, p] = create_plot_graph(Theta)

    dimension = size(Theta,1);

    array = 0:dimension-1;

    alpha = array/dimension*2*pi;

    G = graph(Theta, 'OmitSelfLoops');

    LWidths = 4*G.Edges.Weight/max(G.Edges.Weight);

    p = plot(G);

    p.EdgeLabel = G.Edges.Weight;

    p.LineWidth = LWidths;

    p.XData = cos(alpha);

    p.YData = sin(alpha);

    p.MarkerSize = 10;

    %,'EdgeLabel',G1.Edges.Weight,'LineWidth',LWidths1);

end

 No newline at end of file

%% Project - Time-Varying Graphical Lasso

%%

%% Generate real statistics

dimension = 6;

timestamps1 = 10;

timestamps2 = 10;

total_stamps = timestamps1 + timestamps2;

observations = 10;

inv_sigma1 = [1,0.5,0,0,0,0;0.5,1,0.5,0.25,0,0;0,0.5,1,0,0.25,0;0,0.25,0,1,0.5,0;0,0,0.25,0.5,1,0.25;0,0,0,0,0.25,1];

inv_sigma2 = [1,0,0,0.5,0,0;0,1,0,0,0.5,0;0,0,1,0.5,0.25,0.5;0.5,0,0.5,1,0,0;0,0.5,0.25,0,1,0;0,0,0.5,0,0,1];

sigma1 = inv(inv_sigma1);

sigma2 = inv(inv_sigma2);

datacell1 = cell(timestamps1,1);

datacell2 = cell(timestamps2,1);

for i = 1:timestamps1

    datacell1{i} = mvnrnd(zeros(dimension,1), sigma1, observations);

end

for i = 1:timestamps2

    datacell2{i} = mvnrnd(zeros(dimension,1), sigma2, observations);

end

datacells = [datacell1;datacell2];

%% Empirical covariance matrices

empirical_covariance_matrices = cell(total_stamps,1);

for i = 1:total_stamps

    empirical_covariance_matrices{i} = datacells{i}'*datacells{i};

end

%% Create and plot graph

figure(1);

[G1, p1] = create_plot_graph(inv_sigma1);

figure(2);

[G2, p2] = create_plot_graph(inv_sigma2);