Graph Bayesian Optimization for Multiplex Influence Maximization

• URL: http://arxiv.org/abs/2403.18866v1
• Date: Mon, 25 Mar 2024 14:50:01 GMT
• Title: Graph Bayesian Optimization for Multiplex Influence Maximization
• Authors: Zirui Yuan, Minglai Shao, Zhiqian Chen,
• Abstract summary: Influence (IM) is the problem of identifying a limited number of initial influential users within a social network to maximize the number of influenced users. Previous research has mostly focused on individual information propagation, neglecting the simultaneous and interactive dissemination of multiple information items. This paper first formulates the Multiplex Maximization (Multi-IM) problem using diffusion models with an information association mechanism.
• Score: 9.155955744238852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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