Community Detection Based on Topic Distance in Social Tagging Networks

Hongtao Liu, Hui Chen, Mao Lin, Yu Wu

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


Keywords


community detection; tagging networks; topic distance; modularity optimization

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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