论文标题
基于CNN的网络鲁棒性预测缺失边缘
CNN-based Prediction of Network Robustness With Missing Edges
论文作者
论文摘要
复杂网络的连接性和可控性是两个重要问题，可以保证网络系统运行。连通性和可控性的鲁棒性可确保系统在各种恶意攻击下正常稳定地运行。使用攻击模拟评估网络鲁棒性是很耗时的，而基于卷积的神经网络（CNN）的预测方法提供了一种具有成本效益的方法来近似网络鲁棒性。在本文中，我们调查了基于CNN的连接性和可控性鲁棒性预测方法的性能，当缺少部分网络信息时，即邻接矩阵不完整。进行了广泛的实验研究。探索一个阈值，如果丢失了总数超过7.29 \％的信息，则在实验中所有情况下，基于CNN的预测的性能将显着退化。比较了缺少边缘表示的两种情况，1）在预测输入中，缺失边缘标记为“无边”，而2）使用“未知”的特殊标记表示缺失的边缘。实验结果表明，第一个表示对基于CNN的预测指标误导了。
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.
