Zang Yingbang, Fan Ziye, Wang Zixi, Zheng Yi, Ding Li, Wu Xiaoqun
School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China.
School of Journalism and Communication, Wuhan University, Wuhan 430072, China.
Chaos. 2024 Jul 1;34(7). doi: 10.1063/5.0210741.
Higher-order networks present great promise in network modeling, analysis, and control. However, reconstructing higher-order interactions remains an open problem. A significant challenge is the exponential growth in the number of potential interactions that need to be modeled as the maximum possible node number in an interaction increases, making the reconstruction exceedingly difficult. For higher-order networks, where higher-order interactions exhibit properties of lower-order dependency and weaker or fewer higher-order connections, we develop a reconstruction scheme integrating a stepwise strategy and an optimization technique to infer higher-order networks from time series. This approach significantly reduces the potential search space for higher-order interactions. Simulation experiments on a wide range of networks and dynamical systems demonstrate the effectiveness and robustness of our method.
高阶网络在网络建模、分析和控制方面展现出巨大潜力。然而，重建高阶相互作用仍然是一个悬而未决的问题。一个重大挑战是，随着相互作用中最大可能节点数的增加，需要建模的潜在相互作用数量呈指数增长，这使得重建变得极其困难。对于高阶相互作用呈现出低阶依赖性以及高阶连接较弱或较少特性的高阶网络，我们开发了一种整合逐步策略和优化技术的重建方案，以便从时间序列中推断高阶网络。这种方法显著减少了高阶相互作用的潜在搜索空间。在广泛的网络和动态系统上进行的仿真实验证明了我们方法的有效性和稳健性。
