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Community detection is an important work for discovering the structure and features of complex networks. Many existing methods are sensitive to critical user-dependent parameters or time-consuming in practice. In this paper, we propose a novel label propagation algorithm, called CenLP (Centrality-based Label Propagation). The algorithm introduces a new function to measure the centrality of nodes quantitatively without any user interaction by calculating the local density and the similarity with higher density neighbors for each node. Based on the centrality of nodes, we present a new label propagation algorithm with specific update order and node preference to uncover communities in large-scale networks automatically without imposing any prior restriction. Experiments on both real-world and synthetic networks manifest our algorithm retains the simplicity, effectiveness, and scalability of the original label propagation algorithm and becomes more robust and accurate. Extensive experiments demonstrate the superior performance of our algorithm over the baseline methods. Moreover, our detailed experimental evaluation on real-world networks indicates that our algorithm can effectively measure the centrality of nodes in social networks. [ABSTRACT FROM AUTHOR]