动态信息网络中基于角色的结构演化与预测

Role-Based Structural Evolution and Prediction in Dynamic Networks

动态信息网络是当前复杂网络领域中一个极具挑战的问题,其动态的演化过程具有时序、复杂、多变的特点.结构是网络最基本的特征,也是进行网络建模和分析的基础,研究网络结构的演化过程,对全面认识复杂系统的行为倾向具有重要意义.使用角色来量化动态网络的结构,得到动态网络的角色模型,应用并改进多类标分类问题的问题转换思想,将动态网络的角色预测问题视为多目标回归问题,以历史网络数据作为训练数据构建模型,预测未来时刻网络可能的角色分布情况,提出基于多目标回归思想的动态网络角色预测方法 MTR-RP(multi-target regression based role prediction).该方法不仅克服了基于转移矩阵方法忽略时间因素的不足,还考虑了多个预测目标之间可能存在的依赖关系.实验结果表明,提出的MTR-RP方法具有更准确且更稳定的预测效果.

Dynamic information network is a new challenging problem in the field of current complex networks. The evolution of dynamic networks is temporal, complex and changeable. Structure is the basic characteristics of the network, and is also the basis of network modeling and analysis. The study of the network structure evolution is of great importance in getting a comprehensive understanding of the behavior trend of complex systems. This paper introduces ＂role＂ to quantify the structure of dynamic network and proposes a role-based model. To predict the role distributions of dynamic network nodes in future time, the presented framework views role prediction as a multi-target regression problem, extracts properties from historical snapshot sub-network, and predicts the future role distributions of dynamic network nodes. The paper then proposes a multi-target regression based role prediction （MTR-RP） method for dynamic network. This method not only overcomes the drawback of the existing methods which operate on transfer matrix while ignoring the time factor, but also takes into account of possible dependencies between multiple forecast targets. Experiments results show that MTR-RP has better and more stable prediction capability eomlaared with the existing methods.