基于概率主题模型的社交网络层次化社区发现算法

针对传统的社区发现算法大多基于网络拓扑结构寻找独立的社区结构,忽略了用户兴趣属性,并且不能有效地发现社区间的相关性和层次关系等问题。该文提出一种新型的基于PAM(pachinko allocation model)概率主题模型的层次化网络社区发现算法,综合考虑了用户的兴趣和用户的社交网络关系,在同一模型平台上实现层次化的社区结构发现和用户兴趣挖掘,并捕捉和揭示社区之间的关联性和重叠性等特征。模型采用Gibbs采样方法进行参数推导。在真实数据集上的实验结果验证了所提出算法的可行性和有效性。

Hierarchical Community Discovery for Social Networks Based on Probabilistic Topic Model

The traditional community discovery algorithms are generally based on the link structure of a given social network, they lack of consideration of user's interests and the hierarchical structure of community. In this paper, a novel PAM (Pachinko Allocation Model) probabilistic generative model is proposed to detect latent hierarchical communities based on the user interests and their social relationships. The joint model of topic modeling and community discovery can capture the correlation among multiple communities and their hierarchical structure. Experiments on real-world dataset have confirmed the feasibility and effectiveness of the proposed algorithm.