Dynamic networks are popularly used to describe networks that change with time. Although there have been a large number of research works on understanding dynamic networks using link prediction, node classification and community detection, there is rare work that is specially designed to address the challenge of big network size of dynamic networks. To this end, we study in this paper an emerging and challenging problem of network coarsening in dynamic networks. Network coarsening refers to a class of network “zoom-out” operations where node pairs and edges are grouped together for efficient analysis on big networks. However, existing network coarsening approaches can only handle static networks where network structure weights have been predefined before the coarsening calculation. Under the observation that big networks are highly dynamic and naturally change over time, we consider in this paper to embed information diffusion data which reflect the dynamics of networks for network coarsening. Specifically, we present a new Semi-NetCoarsen approach that jointly maximizes the likelihood of observing the information diffusion data and minimizes the network regularization with respect to the predefined network structural data. The learning function is convex and we use the accelerated proximal gradient algorithm to obtain the global optimal solution. We conduct experiments on two synthetic and five real-world data sets to validate the performance of the proposed method.

动态网络通常用于描述随时间变化的网络。尽管已经有大量关于使用链接预测、节点分类和社区检测来理解动态网络的研究工作，但很少有专门针对动态网络的大网络规模的挑战而设计的工作。为此，我们在本文中研究了动态网络中网络粗化的新兴且具有挑战性的问题。网络粗化是指一类网络“缩小”操作，其中节点对和边被组合在一起以对大型网络进行有效分析。然而，现有的网络粗化方法只能处理在粗化计算之前已经预定义网络结构权重的静态网络。在观察到大网络是高度动态的并且随时间自然变化的情况下，我们在本文中考虑嵌入反映网络动态的信息扩散数据以进行网络粗化。具体来说，我们提出了一个新的Semi-NetCoarsen方法联合最大化观察信息扩散数据的可能性并最小化关于预定义网络结构数据的网络正则化。学习函数是凸的，我们使用加速近端梯度算法来获得全局最优解。我们对两个合成数据集和五个真实世界数据集进行了实验，以验证所提出方法的性能。