  Influence maximization (IM) is the problem of identifying a limited number of
initial influential users within a social network to maximize the number of
influenced users. However, previous research has mostly focused on individual
information propagation, neglecting the simultaneous and interactive
dissemination of multiple information items. In reality, when users encounter a
piece of information, such as a smartphone product, they often associate it
with related products in their minds, such as earphones or computers from the
same brand. Additionally, information platforms frequently recommend related
content to users, amplifying this cascading effect and leading to multiplex
influence diffusion.
  This paper first formulates the Multiplex Influence Maximization (Multi-IM)
problem using multiplex diffusion models with an information association
mechanism. In this problem, the seed set is a combination of influential users
and information. To effectively manage the combinatorial complexity, we propose
Graph Bayesian Optimization for Multi-IM (GBIM). The multiplex diffusion
process is thoroughly investigated using a highly effective global kernelized
attention message-passing module. This module, in conjunction with Bayesian
linear regression (BLR), produces a scalable surrogate model. A data
acquisition module incorporating the exploration-exploitation trade-off is
developed to optimize the seed set further. Extensive experiments on synthetic
and real-world datasets have proven our proposed framework effective. The code
is available at https://github.com/zirui-yuan/GBIM.
