文献类型：期刊文献

英文题名：A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

作者：Liu, Jing[1];Guo, Junfang[2];Li, Qi[1]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing, Zhejiang, Peoples R China;[2]Wenzhou Univ, Dept Comp Sci & Engn, Wenzhou, Zhejiang, Peoples R China

年份：2021

卷号：2021

外文期刊名：COMPLEXITY

收录：SCI-EXPANDED(收录号：WOS:000741320400001)、、EI(收录号：20220311477626)、Scopus(收录号：2-s2.0-85122874636)、WOS

语种：英文

外文关键词：Clustering algorithms - Complex networks

外文摘要：Community structure is one of the most important characteristics of complex networks, which has important applications in sociology, biology, and computer science. The community detection method based on local expansion is one of the most adaptable overlapping community detection algorithms. However, due to the lack of effective seed selection and community optimization methods, the algorithm often gets community results with lower accuracy. In order to solve these problems, we propose a seed selection algorithm of fusion degree and clustering coefficient. The method calculates the weight value corresponding to degree and clustering coefficient by entropy weight method and then calculates the weight factor of nodes as the seed node selection order. Based on the seed selection algorithm, we design a local expansion strategy, which uses the strategy of optimizing adaptive function to expand the community. Finally, community merging and isolated node adjustment strategies are adopted to obtain the final community. Experimental results show that the proposed algorithm can achieve better community partitioning results than other state-of-the-art algorithms.