# Toward Stronger Robustness of Network Controllability: A Snapback Network Model

arXiv: https://arxiv.org/abs/1801.06082 · HTML: https://mlsanity.com/paper/1801.06082/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2018.

## Built on by (most cited first, 6 of 6)

- [A Learning Convolutional Neural Network Approach for Network Robustness Prediction](https://mlsanity.com/paper/2203.10552/) (2022)
- [SPP-CNN: An Efficient Framework for Network Robustness Prediction](https://mlsanity.com/paper/2305.07872/) (2023)
- [A Graph Transformer-Driven Approach for Network Robustness Learning](https://mlsanity.com/paper/2306.06913/) (2023)
- [CNN-based Prediction of Network Robustness With Missing Edges](https://mlsanity.com/paper/2208.11847/) (2022)
- [Importance Ranking in Complex Networks via Influence-aware Causal Node Embedding](https://mlsanity.com/paper/2511.01228/) (2025)
- [High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach](https://mlsanity.com/paper/2603.02265/) (2026)

## 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.