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.
翻译:复杂网络的连通性和可控性是保证网络系统运行的两个重要问题。连接性和可控性保证了系统在各种恶意袭击下正常运行和刺穿。使用攻击模拟评估网络的稳健性耗时,而以进化神经网络为基础的预测方法则提供了一种成本效率方法来估计网络的稳健性。在本文中,我们调查了基于CNN的连通性和可控性强性预测方法的绩效,当部分网络信息缺失时,即对相邻性矩阵不完整时。进行了广泛的实验研究。探索了一个阈值,即如果损失了超过7.29 ⁇ 的信息,那么在实验中所有案例中,CNN的预测性能将大大下降。比较了两种缺失边缘表现的情景:1)在预测投入中,遗漏的边缘是“无边”,2)使用“未知”的特殊标记表示缺失的边缘。实验结果显示,第一个表示对CNN的预测器有误导。