Resting-state fMRI assessed with graph theoretical modeling provides a noninvasive approach for measuring brain network topological organization properties, yet their reproducibility remains uncertain. Here we examined the test-retest reliability of seven brain small-world network metrics from well-controlled resting-state scans of 16 healthy adults using different data processing and modeling strategies. Among the seven network metrics, Lambda exhibited highest reliability whereas Sigma performed the worst. Weighted network metrics provided better reliability than binary network metrics, while reliability from the AAL90 atlas outweighed those from the Power264 parcellation. Global signal regression had no consistent effect on reliability of these network metrics.