Most community detection algorithms for complex networks are focused on nonoverlapping communities. However, there are many overlapping communities in real-world complex networks. To solve the contradiction, this paper develops a novel overlapping community detection algorithm based on Markov chain. First, the input adjacency matrix was expanded to guide the information flow. Then, the inflation operation was implemented to enhance the weakening boundary of communities. After that, an adaptive threshold was introduced to reconstruct the matrix. The network corresponding to the reconstructed matrix displays the overlapping communities in the original network. The proposed algorithm was compared with several popular community detection algorithms on artificial and real-world networks. The results show that our algorithm achieved higher recognition accuracy and faster convergence than the contrastive algorithms.

This paper proposes a method to compare the marks of two bullet heads. First of all, the correction algorithm of the bullet head marks and the denoising model of the matrix low rank decomposition are proposed for the translation and rotation errors of the measured data. Finally, the method to compare the marks of two bullet heads is proposed based on the definition of the similarity of the matrix and the method of sequence alignment. z 423

No abstract