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Influence Maximization(IM) aims to identify highly influential nodes tomaximize influence spread in a network. Previous research on the IM problem hasmainly concentrated on single-layer networks, disregarding the comprehension ofthe coupling structure that is inherent in multilayer networks. To solve the IMproblem in multilayer networks, we first propose an independent cascade model(MIC) in a multilayer network where propagation occurs simultaneously acrossdifferent layers. Consequently, a heuristic algorithm, i.e., Adaptive CouplingDegree (ACD), which selects seed nodes with high spread influence and a lowdegree of overlap of influence, is proposed to identify seed nodes for IM in amultilayer network. By conducting experiments based on MIC, we havedemonstrated that our proposed method is superior to the baselines in terms ofinfluence spread and time cost in 6 synthetic and 4 real-world multilayernetworks.

Influence maximization in multilayer networks based on adaptive coupling degree