# Knowledge-Based Prediction of Network Controllability Robustness

arXiv: https://arxiv.org/abs/2003.08563 · HTML: https://mlsanity.com/paper/2003.08563/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [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)
- [Comprehensive Analysis of Network Robustness Evaluation Based on Convolutional Neural Networks with Spatial Pyramid Pooling](https://mlsanity.com/paper/2308.08012/) (2023)
- [CNN-based Prediction of Network Robustness With Missing Edges](https://mlsanity.com/paper/2208.11847/) (2022)
- [High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach](https://mlsanity.com/paper/2603.02265/) (2026)

## Abstract (arXiv)

Network controllability robustness reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming or even infeasible. In the present paper, an improved method for predicting the network controllability robustness is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor; 2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity.