  Influence Maximization(IM) aims to identify highly influential nodes to
maximize influence spread in a network. Previous research on the IM problem has
mainly concentrated on single-layer networks, disregarding the comprehension of
the coupling structure that is inherent in multilayer networks. To solve the IM
problem in multilayer networks, we first propose an independent cascade model
(MIC) in a multilayer network where propagation occurs simultaneously across
different layers. Consequently, a heuristic algorithm, i.e., Adaptive Coupling
Degree (ACD), which selects seed nodes with high spread influence and a low
degree of overlap of influence, is proposed to identify seed nodes for IM in a
multilayer network. By conducting experiments based on MIC, we have
demonstrated that our proposed method is superior to the baselines in terms of
influence spread and time cost in 6 synthetic and 4 real-world multilayer
networks.
