一种融合信息熵的个人网络主题圈子发现算法

doi:10.3969/j.issn.1001-2400.2017.03.008

Abstract

Abstract:

Due to small scale, large amounts of information, the ego network has become a very important research area. Present community detection algorithms focus mainly on the global large scale network, however existing researches have indicated that the community structure is not obvious as expected on the global network. In this paper a novel circles detection algorithm is proposed, which is devoted to finding the circle structure in the ego network. The proposed algorithm defines a new object function, and the detection of circles could be conducted via optimization of the function heuristically. First, this paper extracts topic distribution from the user generated text, and introduces information entropy to evaluate user topic distribution. Then, the harmonic factor is used to combine structure function and entropy function, which leads to the object function. Finally, the optimization of the object function gives the solution for circle detection. Extensive experiments on weibo dataset demonstrate that the proposed algorithm can effectively mine topic-related circles.

Key words: social network analysis, circle detection, text mining, ego network, entropy

TANG Xing;QUAN Yining;DONG Ze;MIAO Qiguang. Novel algorithm for finding circles in the ego network based on entropy[J].Journal of Xidian University, 2017, 44(3): 43-48.

References

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Novel algorithm for finding circles in the ego network based on entropy

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