Toward Stronger Robustness of Network Controllability: A Snapback Network Model
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jan 2018.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 1; 2026: 1.
6 papers.
- 2022A Learning Convolutional Neural Network Approach for Network Robustness Predictioncited by 6
- 2023SPP-CNN: An Efficient Framework for Network Robustness Predictioncited by 1
- 2023A Graph Transformer-Driven Approach for Network Robustness Learningcited by 1
- 2022CNN-based Prediction of Network Robustness With Missing Edgescited by 0
- 2025Importance Ranking in Complex Networks via Influence-aware Causal Node Embeddingcited by 0
- 2026High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approachcited by 0
- 2026High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach28 Feb 2026
- 2025Importance Ranking in Complex Networks via Influence-aware Causal Node Embedding3 Nov 2025
- 2023A Graph Transformer-Driven Approach for Network Robustness Learning12 Jun 2023
- 2023SPP-CNN: An Efficient Framework for Network Robustness Prediction13 May 2023
- 2022CNN-based Prediction of Network Robustness With Missing Edges25 Aug 2022
- 2022A Learning Convolutional Neural Network Approach for Network Robustness Prediction20 Mar 2022