The Controllability on temporal complex networks is one of the most important challenges among researchers in this field. The primary purpose of network controllability is to apply inputs by selecting minimum driver nodes set (MDS) to the network components to move the network from an initial state to a final state in a limited time. The most important challenges in the controllability of temporal networks can be mentioned the high complexity of the control algorithms used in these methods as well as the high data overhead of temporal network representation models such as the layered model. In this paper, centrality measures are used as the most important characteristics of networks for network controllability. For this purpose, centrality measures have been redefined based on temporal networks and a new controllability method has been proposed based on temporal centrality measures. Then, these properties are used for selecting the minimal driver nodes set, in such a way that the network can be fully controlled using these nodes. The experimental results demonstrate that by using temporal centrality measures the execution speed of control processes is improved (57% improvement) and the overhead is not increased and also the control process has led to the same length of MDS as other conventional controllability methods, it has even been better in some cases.

The Complex Social Internet of Things (CSIoT) integrates the connectivity of IoT with the relational dynamics of complex social networks, creating systems where devices autonomously form and manage relationships. The centrality measures specify the topological characteristics of each node in terms of local and global information of the node in the network. The detection of effective devices in disseminating information across CSIoT networks is critical for optimizing communication, improving network performance, and ensuring efficient resource utilization. In this paper, temporal centrality measures are used to identify influential devices in information dissemination. For this purpose, first, the centrality measures for SIoT network devices have been redefined, and then, using the SIR model, each of the measures has been evaluated in terms of the success rate in identifying effective devices in information dissemination. The results have shown that in SIoT networks that have a high clustering coefficient, the centrality measures of closeness and betweenness have a better performance in identifying influential devices that are effective in spreading information. Also, for networks that have a high degree of heterogeneity, the device coreness centrality and device Katz centrality measures perform better than other measures. Finally, the results show that mobile devices play a more important role in disseminating information than static devices.

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.