Networks are prevalent in many high impact domains. Moreover, cross-domain interactions are frequently observed in many applications, which naturally form the dependencies between different networks. Such kind of highly coupled network systems are referred to as multi-layered networks, and have been used to characterize various complex systems, including critical infrastructure networks, cyber-physical systems, collaboration platforms, biological systems, and many more. Different from single-layered networks where the functionality of their nodes is mainly affected by within-layer connections, multi-layered networks are more vulnerable to disturbance as the impact can be amplified through cross-layer dependencies, leading to the cascade failure to the entire system. To manipulate the connectivity in multi-layered networks, some recent methods have been proposed based on two-layered networks with specific types of connectivity measures. In this paper, we address the above challenges in multiple dimensions. First, we propose a family of connectivity measures (SubLine) that unifies a wide range of classic network connectivity measures. Third, we reveal that the connectivity measures in the SubLine family enjoy diminishing returns property, which guarantees a near-optimal solution with linear complexity for the connectivity optimization problem. Finally, we evaluate our proposed algorithm on real data sets to demonstrate its effectiveness and efficiency.

中文翻译：

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实现多层网络的最佳连接


网络在许多高影响领域中很普遍。此外，在许多应用中经常观察到跨域交互，这自然形成了不同网络之间的依赖关系。这种高度耦合的网络系统被称为多层网络，并已被用来表征各种复杂系统，包括关键基础设施网络、信息物理系统、协作平台、生物系统等等。与单层网络中节点的功能主要受层内连接影响不同，多层网络更容易受到干扰，因为影响可以通过跨层依赖放大，导致整个网络的级联故障。系统。为了操纵多层网络中的连通性，最近提出了一些基于具有特定类型连通性度量的两层网络的方法。在本文中，我们从多个维度解决上述挑战。首先，我们提出了一系列连接措施（SubLine），它统一了广泛的经典网络连接措施。第三，我们揭示了 SubLine 系列中的连通性措施具有收益递减特性，这保证了连通性优化问题的线性复杂度的接近最优解。最后，我们在真实数据集上评估我们提出的算法，以证明其有效性和效率。