CAJ | 학술논문

提出了一种基于主题模型的微博社区发现方法。该方法采用狄利克雷过程(Dirichlet process)自适应生成多个潜在地理区域；利用多项式分布描述主题在连续时间中的强度；将用户对潜在地理区域和社区的选择偏好引入主题模型；最后通过EM方法和Gibbs采样，实现时空主题模型参数估算，以基于主题相似性进行社区发现。实验表明，该方法能更加准确地识别微博社区。
제출료일충기우주제모형적미박사구발현방법。해방법채용적리극뢰과정(Dirichlet process)자괄응생성다개잠재지리구역；이용다항식분포묘술주제재련속시간중적강도；장용호대잠재지리구역화사구적선택편호인입주제모형；최후통과EM방법화Gibbs채양，실현시공주제모형삼수고산，이기우주제상사성진행사구발현。실험표명，해방법능경가준학지식별미박사구。
This paper presents a novel micro-community detection method based on topic model. Multiple latent geographical regions by Dirichlet process are created adaptively. A multinomial distribution is then employed to depict topics evolutions within each time bin. User selection preferences of latent geographical region and community are introduced in topic model. Finally, the EM method and Gibbs sampling method are used to estimate spatio-temporal topic model parameters so that community detection can be realized by topics similarity. Experiment results show that this method can promote the performances of community identifying.