A method based on k-shell decomposition to identify influential nodes in complex networks

Identifying the most influential nodes in complex networks is an open research issue, which can be divided into two sub-problems: identifying and ranking the influential nodes based on their individual influence and selecting a group of nodes for maximum propagation in the network. Prior research has only focused on one of these sub-issues. In this paper, a new method is proposed that measures the spreading power of influential nodes (the first sub-problem) and selects the best group from them (the second sub-problem). The proposed method allocates the input network to different communities and weighs the graph edges using common neighbors and the degrees of the two end vertices in each community. Next, the method measures and ranks the nodes' propagation power in each community and selects a group of influential nodes to initiate the propagation process. The effectiveness of the proposed method is shown through conducting experiments on both synthetic and real networks. The method is compared with other previously known methods based on ranking accuracy, discrimination nodes' ranks, and spread amount of influence. The results show that the proposed method outperforms other methods in all test datasets, indicating its significant superiority in identifying the most influential nodes in complex networks.