Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy.
Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, New York 11201, USA.
Phys Rev E. 2019 Oct;100(4-1):042306. doi: 10.1103/PhysRevE.100.042306.
Many complex systems are characterized by time-varying patterns of interactions. These interactions comprise strong ties, driven by dyadic relationships, and weak ties, based on node-specific attributes. The interplay between strong and weak ties plays an important role on dynamical processes that could unfold on complex systems. However, seldom do we have access to precise information about the time-varying topology of interaction patterns. A particularly elusive question is to distinguish strong from weak ties, on the basis of the sole node dynamics. Building upon analytical results, we propose a statistically-principled algorithm to reconstruct the backbone of strong ties from data of a spreading process, consisting of the time series of individuals' states. Our method is numerically validated over a range of synthetic datasets, encapsulating salient features of real-world systems. Motivated by compelling evidence, we propose the integration of our algorithm in a targeted immunization strategy that prioritizes influential nodes in the inferred backbone. Through Monte Carlo simulations on synthetic networks and a real-world case study, we demonstrate the viability of our approach.
许多复杂系统的特点是随时间变化的相互作用模式。这些相互作用包括由对偶关系驱动的强联系和基于节点特定属性的弱联系。强联系和弱联系之间的相互作用在复杂系统上的动力学过程中起着重要作用。然而，我们很少能够获得关于随时间变化的相互作用模式的拓扑结构的精确信息。一个特别难以捉摸的问题是，仅根据节点动态来区分强联系和弱联系。基于分析结果，我们提出了一种基于统计原理的算法，从传播过程的数据中重建强联系的主干，该传播过程由个体状态的时间序列组成。我们的方法在一系列包含现实系统突出特征的合成数据集上进行了数值验证。受有力证据的启发，我们提出将我们的算法集成到一种有针对性的免疫策略中，该策略优先考虑推断出的主干中具有影响力的节点。通过对合成网络和真实案例研究的蒙特卡罗模拟，我们证明了我们方法的可行性。
