一种集成互信息的改进社区划分算法

Y. Li, Ying Sha, Jixi Shan, Bo Jiang, Jianjun Wu
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引用次数: 1

摘要

社区检测的研究可以帮助我们分析社会网络中的各种问题,其中社区结构的研究是非常重要的。本文提出了一种改进算法:改进的互信息集成BGLLi (improved BGLL integrated Mutual Information, BGLLi),该算法在稳定模块化的同时融合互信息指标,从而找到模块化的最优阈值,有效地减少了社区划分的运行时间。数据集采用Twitter数据集和公开的Arxiv数据集,相关实验结果验证了算法的有效性
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An Improved Community Partition Algorithm Integrating Mutual Information
The research of community detection can help us analyze kinds of problems in social network, in which the research of community structure is very important. This paper proposes an improved algorithm: An Improved BGLL Integrating Mutual Information (BGLLi), which stabilizes modularity, meanwhile fuses the index of mutual information, so that we can find the optimal threshold of modularity and reduce the running time of community partition effectively. The dataset adopts the Twitter dataset and the public Arxiv dataset, and the related experimental results have verified the effectiveness of the algorithm
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