  Evaluation of systemic risk in networks of financial institutions in general
requires information of inter-institution financial exposures. In the framework
of Debt Rank algorithm, we introduce an approximate method of systemic risk
evaluation which requires only node properties, such as total assets and
liabilities, as inputs. We demonstrate that this approximation captures a large
portion of systemic risk measured by Debt Rank. Furthermore, using Monte Carlo
simulations, we investigate network structures that can amplify systemic risk.
Indeed, while no topology in general sense is {\em a priori} more stable if the
market is liquid [1], a larger complexity is detrimental for the overall
stability [2]. Here we find that the measure of scalar assortativity correlates
well with level of systemic risk. In particular, network structures with high
systemic risk are scalar assortative, meaning that risky banks are mostly
exposed to other risky banks. Network structures with low systemic risk are
scalar disassortative, with interactions of risky banks with stable banks.
