for j = 1:5
    tic
    n=N(j);
    [Edges] = generate_network(n, density);
    b = ones(n,1).*(1-d)/n;
    I=eye(n);
    B=zeros(n);
    for i=1:length(Edges)
        B(Edges(2,i),Edges(1,i))=1;
    end
    A=zeros(n);
    for i=1:n
        L=sum(B(:,i));
        A(i,i) = 1/L;
    end
    M = sparse(I - d*B*A);
    A = sparse(A);  % macierze A, B i I muszą być przechowywane w formacie sparse (rzadkim)
    B = sparse(B);
    I = sparse(I);

    L=sparse(tril(M,-1));
    U=sparse(triu(M,1));
    D=sparse(diag(diag(M)));
    r = ones(n,1).*(1/n);
    res = [1 1];
    tic
    % obliczenia
    iter = 0;
    normy = [];
    while res>10e-14
        res = norm(M*r-b);
        r = -(D+L)\(U*r)+(D+L)\b;
        iter = iter+1;
        normy(iter) = res
    end

    iteracje(j) = iter;
    normy_jacobi(j) = semilogy(normy);
    czas_Jacobi(j) = toc;

end

plot(N, czas_Jacobi)
plot(N,iteracje);
plot(N,normy_jacobi);