Henan Key Laboratory on Public Opinion Intelligent Analysis, School of Computer Science, Zhongyuan University of Technology, ZhengZhou, China.
Faculty of Philosophy, Sciences and Letters at Ribeirão Preto (FFCLRP),University of São Paulo (USP), Ribeirão Preto, SP, Brazil.
Sci Rep. 2020 Jan 14;10(1):240. doi: 10.1038/s41598-019-57123-1.
Temporal network mining tasks are usually hard problems. This is because we need to face not only a large amount of data but also its non-stationary nature. In this paper, we propose a method for temporal network pattern representation and pattern change detection following the reductionist approach. The main idea is to model each stable (durable) state of a given temporal network as a community in a sampled static network and the temporal state change is represented by the transition from one community to another. For this purpose, a reduced static single-layer network, called a target network, is constructed by sampling and rearranging the original temporal network. Our approach provides a general way not only for temporal networks but also for data stream mining in topological space. Simulation results on artificial and real temporal networks show that the proposed method can group different temporal states into different communities with a very reduced amount of sampled nodes.
时间网络挖掘任务通常是难题。这是因为我们不仅需要面对大量的数据，还需要面对其非平稳性。在本文中，我们提出了一种基于还原论方法的时间网络模式表示和模式变化检测方法。主要思想是将给定时间网络的每个稳定（持久）状态建模为采样静态网络中的一个社区，并且时间状态变化由从一个社区到另一个社区的转换表示。为此，通过采样和重新排列原始时间网络来构建一个简化的静态单层网络，称为目标网络。我们的方法不仅为时间网络，而且为拓扑空间中的数据流挖掘提供了一种通用方法。在人工和真实时间网络上的仿真结果表明，所提出的方法可以使用非常少的采样节点将不同的时间状态分组到不同的社区中。
