School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
Department of Physics, Arizona State University, Tempe, Arizona 85287, USA.
Phys Rev E. 2018 Mar;97(3-1):032317. doi: 10.1103/PhysRevE.97.032317.
Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.
从观测数据中揭示复杂网络系统的结构和动态是当前的研究热点。是否有可能开发一个完全基于数据的框架来破译复杂网络上的网络结构和不同类型的动力过程？我们开发了一种名为稀疏动力玻尔兹曼机（SDBM）的模型，作为承载二进制动力过程的复杂网络的结构估计器。SDBM 根据原始系统的拓扑结构获得其拓扑结构，并能够模拟原始的二进制动力过程。我们开发了一种基于压缩感知和聚类算法的全自动方法来构建 SDBM。我们展示了，对于模型和现实世界复杂网络上的各种代表性动力过程，等效的 SDBM 可以恢复原始系统的网络结构，并以高精度模拟其动力行为。
