  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.
