Precisely estimating factor scores is challenging, especially when models are mis-specified. Stemming from network analysis, centrality measures offer an alternative approach to estimating the scores. Using a two-fold simulation design with varying availability of a priori theoretical knowledge, this study implemented hybrid centrality to estimate factor scores and compared the performance with traditional methods, where both proper and improper specifications were considered. In supervised scenarios, network scores using hybrid centrality performed similarly to CFA scores for correctly specified models. The network scores were more accurate when sample sizes were small or test reliability was low, and also demonstrated higher robustness under misspecification. In the second fold, network scores performed better than EFA scores when structure knowledge was unavailable, as the LoGo algorithm of network analysis demonstrated the best performance in retaining the correct factor structure. The results suggest that network analysis can be a better choice for factor score estimation under various conditions.