Toward Stronger Robustness of Network Controllability: A Snapback Network Model

Abstract

A new complex network model, called q-snapback network, is introduced. Basic topological characteristics of the network, such as degree distribution, average path length, clustering coefficient and Pearson correlation coefficient, are evaluated. The typical 4-motifs of the network are simulated. The robustness of both state and structural controllabilities of the network against targeted and random node- and edge-removal attacks, with comparisons to the multiplex congruence network and the generic scale-free network, are presented. It is shown that the q-snapback network has the strongest robustness of controllabilities due to its advantageous inherent structure with many chain- and loop-motifs.

13 pages, 11 figures

References in corpus (2)

• Emergence of bimodality in controlling complex networks
• Network Controllability Is Determined by the Density of Low In-Degree and Out-Degree Nodes

Cited by in corpus (10)

• Predicting Network Controllability Robustness: A Convolutional Neural Network Approach
• Structural Robustness of Complex Networks: A Survey of A Posteriori Measures
• A Learning Convolutional Neural Network Approach for Network Robustness Prediction
• Knowledge-Based Prediction of Network Controllability Robustness
• Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition
• A Framework of Hierarchical Attacks to Network Controllability
• SPP-CNN: An Efficient Framework for Network Robustness Prediction
• Observability Robustness under Sensor Failures: a Computational Perspective
• CNN-based Prediction of Network Robustness With Missing Edges
• Towards the cycle structures in complex network: A new perspective