# Graph Bayesian Optimization for Multiplex Influence Maximization

arXiv: https://arxiv.org/abs/2403.18866 · HTML: https://mlsanity.com/paper/2403.18866/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Scalable Bayesian Optimization Using Deep Neural Networks](https://mlsanity.com/paper/1502.05700/) (2015)
- [Deep Graph Representation Learning and Optimization for Influence Maximization](https://mlsanity.com/paper/2305.02200/) (2023)
- [Deep Neural Architecture Search with Deep Graph Bayesian Optimization](https://mlsanity.com/paper/1905.06159/) (2019)
- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [From Competition to Complementarity: Comparative Influence Diffusion and Maximization](https://mlsanity.com/paper/1507.00317/) (2015)

## Built on by (most cited first, 4 of 4)

- [GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereum](https://mlsanity.com/paper/2405.19971/) (2024)
- [Identifying Super Spreaders in Multilayer Networks](https://mlsanity.com/paper/2505.20980/) (2025)
- [Global Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representations](https://mlsanity.com/paper/2511.07734/) (2025)
- [Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks](https://mlsanity.com/paper/2606.08306/) (2026)

## Abstract (arXiv)

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.