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Issue No. 10 - Oct. (2017 vol. 29)
ISSN: 1041-4347
pp: 2332-2346
Chen Chen , Arizona State University, Tempe, AZ
Jingrui He , Arizona State University, Tempe, AZ
Nadya Bliss , Arizona State University, Tempe, AZ
Hanghang Tong , Arizona State University, Tempe, AZ
ABSTRACT
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.
INDEX TERMS
Optimization, Silicon, Power generation, Transportation, Weight measurement, Collaboration, Complexity theory
CITATION
Chen Chen, Jingrui He, Nadya Bliss, Hanghang Tong, "Towards Optimal Connectivity on Multi-Layered Networks", IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. , pp. 2332-2346, Oct. 2017, doi:10.1109/TKDE.2017.2719026
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