Multiplex Interdependence Centrality: Quantifying Cross-Layer Contagion Risk in Financial Networks

Abstract

Financial institutions today are embedded in a multiplex network of interconnected obligations spanning interbank lending, sovereign bond exposures, and decentralized finance liquidity pools. Traditional systemic risk metrics treat each channel independently, ignoring the cross-layer feedback mechanisms through which shocks amplify during crises. We introduce the Multiplex Interdependence Centrality framework, a spectral measure that computes the principal eigenvector of a weighted supra-adjacency matrix coupling multiple financial layers. The multiplex interdependence centrality score captures a node's systemic importance jointly across all layers, accounting for both intra-layer exposure weights and inter-layer coupling intensities. We couple this centrality measure with a threshold-based cascade simulation to validate its predictive power. Using a synthetic three-layer financial network of 100 nodes representing interbank lending, sovereign bonds, and DeFi markets, we demonstrate that MIC achieves a Pearson correlation of r = 0.806 with actual cascade damage that substantially outperforms the centrality of the eigenvector of the single-layer, PageRank, and the centrality of the differences. Our results provide a rigorous quantitative foundation for integrating multiplex network metrics into institutional risk monitoring, central bank stress-testing frameworks, and regulatory oversight of cross-sector financial contagion.