Before revision:  We applied this technique and studied the performance and convergence behavior of the algorithm with different structure of the financial network  .
After revision:  We applied this technique and studied the performance and convergence behavior of the algorithm with different structure of the financial network are important elements to measure systemic risk and identify SIFIs. Results show that increasing the levels of out- and in-degree connections of an institution can have a diverse impact on its systemic ranking. Additionally, on the empirical side, we investigate the factors which lead to the identification of Global Systemic Important Banks (G-SIB) by using a panel dataset of the largest banks in each country. Our empirical results supports the main findings of the theoretical model .