Community detection has a wide variety of applications in different fields such as data mining, social network analysis and so on.
Label Propagation Algorithm (LPA) is a simple and fast community detection algorithm, but it has low accuracy. There have been presented some advanced versions of LPA in recent years such as CenLP and WILPAS. In this paper, we present improved versions of CenLP and WILPAS methods called CenLP+ and WILPAS+ respectively. Experiments and benchmarks demonstrate that while CenLP+ is as fast as CenLP, it outperforms CenLP on both synthetic and real-world networks. Moreover, while accuracy of WILPAS+ on synthetic networks comparable with that of WILPAS, on real-world networks, WILPAS+ excels WILPAS. In addition, whereas both presented methods CenLP+ and WILPAS+ show high accuracy on synthetic networks, on real-world networks they outperform remarkably all other tested label propagation based algorithms for community detection. Therefore, since CenLP+ and WILPAS+ are both fast and accurate, specially on real-world networks, they can efficiently reveal community structures of mega-scale social networks.