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Hongtao Liu
Chongqing University of Posts and Telecommunications

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Community Detection Based on Topic Distance in Social Tagging Networks Hongtao Liu; Hui Chen; Mao Lin; Yu Wu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 5: May 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Research on the community detection in social tagging networks has attracted much attention in the last decade. Extracting the hidden topic information from tags provides a new way of thinking for community detection in social tagging networks. In this paper, a topic tagging network by extracting several topics from the tags through using the Latent Dirichlet Allocation (LDA) model is built firstly. Then a topic distance between users is defined, which depends on the bookmarking relationships between users and tags. Further, a modularity clustering approach based on the topic distance is proposed to detect communities in social tagging networks. Empirical studies on real-world networks demonstrate that the proposed method can effectively detect communities in tagging networks. DOI ; http://dx.doi.org/10.11591/telkomnika.v12i5.4170
Interest Excitation Propagation Model for Information Propagation on micro-blogging Hongtao Liu; Hongfeng Yun; Hui Chen; Zhaoyu Li; Yu Wu
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 9: September 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

An Information propagation model of micro-blogging is proposed for distinguishing normal and non-normal micro-blogging based on users’ interest excitation, which is abbreviated as IEPM(Interest Excitation Propagation Model for Information).The parameters of the model are clearly associated with the actual propagation and can reflect the characteristics of the propagation feature. The model can distinguish users’ non-autonomous behavior in the propagation process of micro-blogging, which can preliminarily judge that it is a non-normal marketing micro-blogging when it doesn’t meet the general propagation in the model. Quantitative analysis and experiment is performed with the dataset from the representative and typical non-normal micro-blogging in Sina micro-blogging, one of the most popular micro-blogging in China. The results show that the model can better reveal the general propagation laws of micro-blogging, and can distinguish normal and non-normal micro-blogging, which will have theoretical and practical significance to a certain degree. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.2734
Co-Authors Hongfeng Yun Hui Chen Mao Lin Yu Wu Zhaoyu Li