隐马尔可夫模型演化下的隐组查询

针对社会网络图中的隐组查询问题,提出了一种基于隐马尔科夫模型演化的方法。不同于传统方法,文中首先对“微观法则”提出了一些合理的假设,这些法则决定了在某时刻一个个体是否存在于一个特定群体。通过这些假设,可以得到社会个体和群体的动态演化。然后根据群体演化,找出长时间保持通信的群体作为潜在的隐组,再通过进一步分析,确保这些潜在的隐组以一个较高的概率成为理想的结果。为验证算法的有效性,文中分别对模拟和真实的数据进行了测试。

Evolution of Hidden Markov Model for Hidden Group Detection

An approach to hidden group detection in social network based on hidden Markov evolution model is proposed. Different from conventional methods, we begin with reasonable assumptions for the micro-laws to deter- mine whether at any given time a particular individual is in a community or not, based on which we are able to dis- cover the individual dynamics that drive the evolution of the social groups in a community. Finally, we identify per- sistent groups over a time period long enough as potential hidden groups. Further analysis is made to ensure the high probability of these groups to be satisfactory results. Experiments on synthetic data as well as real communities （ e. g. Enron email） are performed.