社会化媒体大数据多阶段整群抽样方法

在线社会化媒体大数据是行动者自组织关系的集合，其内部蕴含了多层次的社会实体关系，因此，在线社会化媒体大数据抽样方法的研究对于社会计算这一新兴研究领域具有重要的理论和应用价值.现有抽样方法存在大型马尔可夫链难以并行化、样本局部性陷入、马尔可夫链燃烧预热等问题.针对这些问题，提出了在线社会化媒体大数据整群多阶段抽样方法OSM-MSCS.该方法首先进行整群分解，将总体分解成若干小型凝聚子群；而后，使用动态延迟拒绝方法对凝聚子群内部的关系抽样；最后，使用Gibbs方法完成不同凝聚子群之间相干关系的筛选，从而获得整个样本序列.实验结果表明，OSM-MSCS方法能够有效地对各种结构特征的在线社会化媒体大数据进行抽样，从“个体地位-群体凝聚性-整体结构性”这3个层次进行综合评价，其抽样效果要明显好于MHRW和BFS这两种最主流的抽样方法.

Sampling Online Social Media Big Data Based Multi Stage Cluster Method

The big data from online social media represents the relationship between the actors' self-organization. It contains multi-level social entity relationship. As an emerging field in recent years, online social media sampling method has important research value and practical significance in social computing. However, there are some problems in existing methods. For example, large Markov chain is difficult to parallelize, sampling is easy to be trapped in local, and there is concerns with Markov chain burn-in process. To address those issues, the paper presents a multi stage cluster sampling for online social media big data (OSM-MSCS). The proposed method first decomposes integral cluster into small cohesive subgroups, then uses delay rejection (DR) to sample typical online social relationship with parallel processing, and finally uses Gibbs sampling methods to choose interaction relationship in different cohesive subgroups to obtain the random sequence. Experimental results show that OSM-MSCS is an effective method for online social media big data, and its sampling technique is better than BFS and MHRW.