An effective network perspective focused on measuring directional interactions of electroencephalographic in different cortical regions during a sustained attentive task requiring vigilance. A novel measure referred to as dynamic partial directed coherence was used to map the cognitive state of vigilance based on graph theory. In the right parieto-occipital area, the area is significantly higher than in other regions of interest (the areas are 0.601 and 0.632 for out-degree and in-degree, respectively). A similar analysis in the right fronto-central area revealed significant differences in the different cognitive states. Across the six regions of interest, significant differences of in-degree and out-degree based alpha band are observed in the right fronto-central and the right parieto-occipital (P < 0.05). The performance was compared with those from a support vector machine using different network-based phase-locking values, partial directed coherence, and dynamic partial directed coherence. Results show that dynamic partial directed coherence can provide more information about direction (compared with phase-locking values) and accuracy (when compared with partial directed coherence). The graph theoretical analysis shows that the effective network based dynamic partial directed coherence has a small-world property for synchronizing neural activity between brain regions. Moreover, the alpha band is well correlated with the cognitive state compared to other frequency bands.