CNN-based Prediction of Network Robustness With Missing Edges

TL;DR

This paper evaluates CNN-based methods for predicting network robustness in the presence of incomplete adjacency matrices, revealing significant performance degradation when more than 7.29% of network information is missing.

Contribution

It investigates the impact of incomplete network data on CNN-based robustness prediction and compares different missing edge representations, providing insights for practical applications.

Findings

Performance drops significantly when over 7.29% of data is missing.

Marking missing edges as 'no edge' misleads CNN predictors.

Using 'unknown' as a marker yields better prediction accuracy.

Abstract

Connectivity and controllability of a complex network are two important issues that guarantee a networked system to function. Robustness of connectivity and controllability guarantees the system to function properly and stably under various malicious attacks. Evaluating network robustness using attack simulations is time consuming, while the convolutional neural network (CNN)-based prediction approach provides a cost-efficient method to approximate the network robustness. In this paper, we investigate the performance of CNN-based approaches for connectivity and controllability robustness prediction, when partial network information is missing, namely the adjacency matrix is incomplete. Extensive experimental studies are carried out. A threshold is explored that if a total amount of more than 7.29\% information is lost, the performance of CNN-based prediction will be significantly degenerated for all cases in the experiments. Two scenarios of missing edge representations are compared, 1) a missing edge is marked `no edge' in the input for prediction, and 2) a missing edge is denoted using a special marker of `unknown'. Experimental results reveal that the first representation is misleading to the CNN-based predictors.