基于超网络的Web社会群体复杂关系建模

Complex Relationships of Web Social Cluster Modeling Based on Supernetwork

针对现有的单一要素表征的集群关系不能反映现实社会群体关系的真实情况和整体结构,采用超网络思想表征网络实体间关于兴趣相似度的隐性联系,再结合显性的社交关系,构造了多维社会集群复杂关系模型,并设计相应的算法和实验证明了模型的可行性与有效性.模型不仅能多维度、更真实地表征网络社群的关系,还能作为预测潜在网络热点话题规模的依据,为准确挖掘潜在的网络结构、进行有效的舆情分析奠定了基础.

Existing methods are almost based on the single factor in characterizing the relationship within the cluster which cannot truly reflect the actual Web cluster＇s structure.Aiming at the problem this paper put forward a supernetwork model to characterize the implicit relationship of Web entities corresponding to their interests.Recombining with dominant social relationships the paper constructed a more realistic multi-dimensional relationship model to characterize the Web cluster relations.Algorithm and experiments were given to illustrate the model＇s effectiveness.The model not only characterizes a more realistic and precise network structures,but also allows detecting network hotspot information which leads a better understanding of public opinion to pave the way to effective analysis on network public opinion.