关键词: 语义社区发现, LDA主题模型, 内容相似度, 标签传播策略, 传播影响力

Abstract: Information network is a kind of complex network with semantic information. The semantic community detection of information network is a new research direction. The complexity of community detection algorithm is increased by considering the node content. Therefore this paper proposes a label propagation algorithm which is suitable for dealing with large scale information network in linear time. Firstly, the latent Dirichlet allocation topic model is used to represent the node content. Secondly, the multiplicative model of content similarity and propagation influence is taken as the label propagation strategy. And the content and the network topology are combined naturally in the normalization. Thirdly, the algorithm updates the node label while the node and the vast majority of neighbors are not the same. Extensive experiments on 12 real-world datasets with varying sizes and characteristics validate the proposed method outperforms other baseline algorithms in quality.

Key words: semantic community detection, latent Dirichlet allocation topic model, content similarity, label propagation strategy, influence propagation