Robust Controllability Network Method on Temporal Network Using Temporal Link Prediction and Network Embedding

Controllability in temporal networks is considered one of the most important challenges in this type of network. Network controllability methods try to fully control the network with the minimum number of control nodes. This type of network is always exposed to internal and external attacks and failures. Therefore, controllability processes need recovery mechanisms to be resistant to various types of failures. The high volume of temporal networks causes the recovery controllability processes to be disrupted. In the paper, a novel method of recovery controllability in temporal networks is proposed to improving controllability network robustness. To restore controllability of the network, the RCTE framework is proposed, in which a temporal network is converted into snapshots at discrete times and then its dimensions are reduced by using network embedding. Finally, using link prediction based on local and global similarity, links that are subject to failure are identified. The effectiveness of the proposed method against various network attacks has been evaluated and compared with other conventional methods. The results show that the RCTE framework performed better than other conventional methods. Also, the proposed method has more controllability and tolerance against malicious attacks compared to other recovery methods.