摘要:动态信息网络是当前复杂网络领域中极具挑战的新问题之一，对其动态的演化过程进行研究，有助于分析网络结构、理解网络特性、发现网络中潜在的信息及演化规律，具有重要的理论意义与应用价值.基于网络结构本身量化表示的复杂性以及网络演化时序、复杂、多变的挑战，使用角色来量化动态网络的结构，并对模型进行分析，给出了两种角色解释的方法；在角色发现的基础上，将动态网络结构预测问题转换为可以表示结构特征的角色预测问题，通过向量自回归的方法，以历史网络角色分布矩阵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，提出了基于潜在角色的动态网络结构预测方法LR-DNSP（latent role based dynamic network structure prediction）.该方法克服了已有基于转移矩阵方法忽略历史信息的不足，并且考虑了多个预测目标之间可能存在的相互关系.实验结果表明，提出的LR-DNSP方法具有更准确的预测效果.



Abstract:Dynamic information network is a new challenging problem in the field of current complex networks. Research on network evolution contributes to analyzing the network structure, understanding the characteristics of the network, and finding hidden network evolution rules, which has important theoretical significance and application value. The study of the network structure evolution is of great importance in getting a comprehensive understanding of the behavior trend of complex systems. However, the network structure is difficult to represent and quantify. And the evolution of dynamic networks is temporal, complex, and changeable, which increases the difficulty in analysis. This study introduces "role" to quantify the structure of dynamic networks and proposes a role-based model, which provides a new idea for the evolution analysis and prediction of network structure. As for the model, two methods to explain the role are given. To predict the role distributions of dynamic network nodes in future time, this study transforms the problem of dynamic network structure prediction into role prediction, which can represent the structural feature. The model extracts properties from historical snapshots of sub-network as the training data and predicts the future role's distributions of dynamic network by using the vector autoregressive method. This study also proposes the method of dynamic network structure prediction based on latent roles (LR-DNSP). This method not only overcomes the drawback of existing methods based on transfer matrix while ignoring the time factor, but also takes into account of possible dependencies between multiple forecast targets. Experimental results show that the LR-DNSP outperforms existing methods in prediction accuracy.



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