论文标题
通过增强边缘提高网络鲁棒性,同时保持强大的结构可控性
Improving Network Robustness through Edge Augmentation While Preserving Strong Structural Controllability
论文作者
论文摘要
在本文中,我们考虑了具有Laplacian动力学的代理网络,并通过在网络中添加最大数量的边缘来研究网络鲁棒性的问题,同时同时保留其强结构可控性(SSC)的下限。 Edge扩大增加了网络对噪声和结构变化的鲁棒性,但是,它也可能恶化网络可控性。因此,通过在图中利用网络可控性与节点之间的距离之间的关系,我们制定了一个边缘增强问题,并具有约束,以保持某些节点对之间的距离,从而保证即使在添加边缘后,SSC的下限也可以保持SSC的下限。在这个方向上,首先,我们选择一个节点对,并最大程度地添加边缘,同时保持选定节点之间的距离。我们表明,最佳解决方案属于称为集团链的某些类别的图形。然后,我们提出了一种算法来添加边缘,同时保留某些节点集合之间的距离。此外,我们提出了一种随机算法,该算法保证了所需的近似值,较高的可能性可以解决边缘增强问题。最后,我们在各种网络上评估了我们的结果。
In this paper, we consider a network of agents with Laplacian dynamics, and study the problem of improving network robustness by adding a maximum number of edges within the network while preserving a lower bound on its strong structural controllability (SSC) at the same time. Edge augmentation increases network's robustness to noise and structural changes, however, it could also deteriorate network controllability. Thus, by exploiting relationship between network controllability and distances between nodes in graphs, we formulate an edge augmentation problem with a constraint to preserve distances between certain node pairs, which in turn guarantees that a lower bound on SSC is maintained even after adding edges. In this direction, first we choose a node pair and maximally add edges while maintaining the distance between selected nodes. We show that an optimal solution belongs to a certain class of graphs called clique chains. Then, we present an algorithm to add edges while preserving distances between a certain collection of nodes. Further, we present a randomized algorithm that guarantees a desired approximation ratio with high probability to solve the edge augmentation problem. Finally, we evaluate our results on various networks.