•We explore the links among US equity and commodity markets via complex network theory.•We reveal the temporal dimension of correlation using time-varying network topologies.•Via simulation analysis we assess the impact of denoising on data dependence structure.•The disparity of entropy centrality measurements is shown between pre- and post-crisis.•The results enable robust mapping of contagion effects incorporating agent expectations. This paper investigates the dynamic causal linkages among U.S. equity and commodity futures markets via the utilization of complex network theory. We make use of rolling estimations of extended matrices and time-varying network topologies to reveal the temporal dimension of correlation and entropy relationships. A simulation analysis using randomized time series is also implemented to assess the impact of de-noising on the data dependence structure. We mainly show evidence of emphasized disparity of correlation and entropy-based centrality measurements for all markets between pre- and post-crisis periods. Our results enable the robust mapping of network influences and contagion effects while incorporating agent expectations.