Abstract
Due to the randomness of label propagation algorithm, the stability is poor and the accuracy is low for community detection results in complex networks. In order to solve the problem, this paper proposes a novel community detection algorithm based on Jaccard similarity label propagation. Firstly, the Jaccard similarity is used to measure nodes importance. Then, the importance of nodes is utilized to reduce the randomness in label selection. Finally, nodes with the highest importance are selected to update labels in iteration, which improves the stability for community detection. Stability and accuracy of data sets of real networks are measured by modularity and normalized mutual information, respectively. Experimental results show that, the proposed algorithm for community detection results is more stable and more accurate than LPA, LPA_SI and KLPA algorithms in the cases of near linear time complexity.
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Acknowledgment
This work was supported by the 2016 Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education Project No. CRKL160102 and 2016 Guangxi Science and Technology Project No. AB16380264.
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Wang, M., Cai, X., Zeng, Y., Liang, X. (2017). A Community Detection Algorithm Based on Jaccard Similarity Label Propagation. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_6
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DOI: https://doi.org/10.1007/978-3-319-68935-7_6
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