The ample evidence that hemodynamics is involved in the local onset and progression of vascular disease, makes the study of cardiovascular flows of wide interest. To date, the large amount of hemodynamic data has been processed through conventional quantification and visualization paradigms, which permit only partially to exploit all the information regarding how blood flow patterns evolve and correlate in complex vascular geometries and during the progression of a pathological condition. In this study, with the intent to uncover and highlight the spatiotemporal evolution and the level of organization of critical blood flow patterns, an innovative approach is proposed combining correlation-based analysis and complex networks (CNs) theory with computational hemodynamics.