Banerjee Soumya Jyoti, Sinha Saptarshi, Roy Soumen
Bose Institute, 93/1 Acharya Prafulla Chandra Roy Road, Kolkata 700 009, India.
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Feb;91(2):022807. doi: 10.1103/PhysRevE.91.022807. Epub 2015 Feb 10.
We propose a network metric, edge proximity, P(e), which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. The effects of removing edges with high P(e) might initially seem inconspicuous but are eventually shown to be very harmful for networks. Compared to existing strategies, the removal of edges by P(e) leads to a remarkable increase in the diameter and average shortest path length in undirected real and random networks till the first disconnection and well beyond. P(e) can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by P(e) causes notable efficiency loss in U.S. and European power grid networks. P(e) identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections and important portions of the neural and brain networks, respectively. Energy flow interactions identified by P(e) form the backbone of long food web chains. Finally, we scrutinize the potential of P(e) in edge controllability dynamics of directed networks.
我们提出了一种网络度量——边接近度,即P(e),它展示了网络中特定边的重要性,而现有网络度量迄今尚未捕捉到这一点。移除具有高P(e)的边的影响最初可能看似不明显,但最终被证明对网络非常有害。与现有策略相比,通过P(e)移除边会导致无向真实网络和随机网络的直径和平均最短路径长度显著增加,直至首次断开连接,甚至在断开连接之后。P(e)可以持续用于将网络分裂成两个几乎相等的部分,从而呈现出一种非常有效的策略来极大地损害网络。以P(e)为目标会导致美国和欧洲电网网络出现显著的效率损失。P(e)在蛋白质-蛋白质相互作用网络中识别出具有基本细胞功能的蛋白质。它分别精确指出调节性神经连接以及神经和大脑网络的重要部分。由P(e)识别出的能量流相互作用构成了长食物网链的主干。最后,我们审视了P(e)在有向网络的边可控性动力学方面的潜力。