CIM: clique-based heuristic for finding influential nodes in multilayer networks

摘要

Identifying Influential nodes (Influence maximization) in complex networks is an essential factor for spreading and controlling the information spreading dynamics in social networks. The majority of the influence maximization problems are in monolayer networks. After advancements and increased social network usage, the need to perform influence maximization in multilayer networks has increased. The critical issue is to identify the influential nodes that can effectively spread the information across the networks. Detecting such influential nodes with high precision in multilayer networks is a challenging and yet unexplored task. Based on our experiments, it is observed that a potential node may have strong connections in both the interlayers and intralayers. A comparative study of various influence maximization algorithms in multilayer networks is carried out with this observation. We propose a novel algorithm, clique-based influence maximization (CIM) in a multilayer network. We also propose ignoring noted nodes in the network to increase the efficiency of influence maximization and remove the information redundancy. CIM is generating better results for influence spread in multilayer networks compared to the other algorithms. The simulation studies have shown that CIM can detect influential nodes on both real and synthetic networks under various environments.