A Learning Convolutional Neural Network Approach for Network Robustness Prediction

TL;DR

This paper introduces a convolutional neural network-based method for efficiently predicting network robustness, outperforming existing approaches and applicable to various network types, thus enabling faster and more accurate robustness assessments.

Contribution

The paper develops LFR-CNN, a novel CNN-based approach that predicts network robustness from compressed features, improving accuracy and efficiency over prior methods.

Findings

LFR-CNN outperforms state-of-the-art prediction methods.

It is insensitive to network size variations.

It predicts robustness faster than attack simulations.

Abstract

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors; 2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability; 3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations; 4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures.