A novel recovery controllability method on temporal networks via temporal lost link prediction

Citations Over Time

Abstract

Abstract Temporal networks are essential in representing systems where interactions between elements evolve over time. A crucial aspect of these networks is their controllability the ability to guide the network to a desired state through a set of control inputs. However, as these networks evolve, links between nodes can be lost due to various reasons, such as network failures, disruptions, or attacks. The loss of these links can severely impair the network’s controllability, making it challenging to recover desired network functions. In this paper, while investigating the destructive effects of various attacks on controllability processes in temporal networks, a new controllability recovery method is proposed, in which it prevents disruptions in this type of network processes by predicting lost links. In the proposed method, using network embedding and feature extraction, the dissimilarity of the nodes is calculated and then the missing links are predicted by designing a neural network. The results of the implementation of the proposed method on the datasets have demonstrates that the proposed method performed better than other conventional methods.

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