Community detection is to detect groups consisting of densely connected nodes, and having sparse connections between them. Many researchers indicate that detecting community structures in complex networks can extract plenty of useful information, such as the structural features, network properties, and dynamic characteristics of the community. Several community detection methods introduced different similarity measures between nodes, and their performance can be improved. In this paper, we propose a community detection method based on an improvement of node similarities. Our method initializes a level for each node and assigns nodes into a community based on similarity between nodes. Then it selects core communities and expands those communities by layers. Finally, we merge communities and choose the best community in the network. The experimental results show that our method achieves state-of-the-art performance.
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