INDEQS introduces a graph-based neural controlled differential equation framework that incorporates prior directed graph knowledge at architectural levels. It separates inner and outer mixing, offering both graph-constrained and data-adaptive variants, with outer informedness reducing mean absolute error on larger graphs, while inner informedness provides parameter efficiency for known adjacency adherence. Continuous decoders outperform discrete ones in real-world traffic and hydrological forecasting tasks.