Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (5): 107-114.DOI: 10.3778/j.issn.1002-8331.1911-0452

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Topic Community Discovery Model Incorporating Topic Similarity Weight

QIAN Yunyun, YANG Wenzhong, YAO Miao, LI Hailei, CHAI Yachuang   

  1. 1.College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
    2.School of Software, Xinjiang University, Urumqi 830046, China
  • Online:2021-03-01 Published:2021-03-02



  1. 1.新疆大学 信息科学与工程学院,乌鲁木齐 830046
    2.新疆大学 软件学院,乌鲁木齐 830046


Social network structure is complex, and the topic community is one of the important ways for personalized recommendation and business promotion. However, the existing methods of topic community mining are either only based on link relationship and text information mining topic community, or mining the topic based on the divided community, the interaction between topic and the community is ignored, which results in the low similarity of the topic within the community. Therefore, it proposes a new community topic computing method, then establishes a topic community discovery model (TSWTCD) that integrates topic similarity weights. The topic is extracted by text information, the topic similarity between nodes is calculated as link weight, and link weight is taken as module parameter to divide the community. Finally, the community topic is obtained according to the proposed new community topic calculation method. Experimental results based on real data set show that the TSWTCD model improves the quality of mining topic communities.

Key words: topic community, links information, topic similarity, modularity



关键词: 主题社区, 链接信息, 主题相似度, 模块度