A method based on link prediction for identifying set of super-spreaders in complex networks

Abstract Identifying a group of key nodes with enormous capability for spreading information to other network nodes is one of the favourable research topics in complex networks. In most existing methods, only the current status of the network is used for identifying and selecting the member of these groups. The main weakness of these methods is a lack of attention to the highly dynamic nature of complex networks and continuous changes in them in terms of creating and eliminating nodes and links. This matter makes the selected group have no proper performance in spreading information relative to other nodes. Therefore, this article presents a novel method for identifying spreader nodes and selecting a superior set from them. In the proposed method, the diffusion power of network nodes is calculated in the first step, and some are selected as influential nodes. In the following steps, it is tried to modify the list of selected nodes by predicting the network variation. Six datasets gathered from real-world networks are utilized for evaluation. The proposed method and other methods are tested to evaluate their spread of influence and time complexity. Results show that using the link prediction in the proposed method can enhance the spread of influence by the selected set compared to other methods so that the spread of influence in some datasets is more than 30$\%$. On the other hand, the time complexity of the proposed method confirms its utility in very large networks.