Optimizing Multiplex Influence Maximization in Social Networks through Graph Bayesian Optimization

Основные понятия

The core message of this paper is to introduce the Multiplex Influence Maximization (Multi-IM) problem, which aims to maximize the influence spread of multiple interconnected information items in a social network, and to propose a Graph Bayesian Optimization framework (GBIM) to effectively solve this problem.

Аннотация

This paper introduces the Multiplex Influence Maximization (Multi-IM) problem, which models the scenario where multiple information items propagate and interact in a multiplex social network. The authors propose a Graph Bayesian Optimization framework (GBIM) to address this problem:

1. The Multi-IM problem formulation incorporates a multiplex diffusion model with an information association mechanism to capture the complex dynamics of multi-item influence spread.

2. The GBIM framework includes two key components:

   • Surrogate Model: A global kernelized attention message-passing module is used to learn the multiplex diffusion process, and this is combined with Bayesian linear regression to create a scalable surrogate model.
   • Data Acquisition: An acquisition function with an explore-exploit trade-off is developed to efficiently optimize the seed set of users and information items.

3. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and scalability of the proposed GBIM framework, outperforming traditional and learning-based influence maximization methods.

4. The authors also provide analysis on the impact of the exploitation rate in the data acquisition module and the scalability of GBIM as the network size increases.

Статистика

The number of users in the Ciao dataset is 7,317, with 170,410 user edges.
The number of items in the Ciao dataset is 404, with 1,018 item edges.
The number of users in the Epinions dataset is 18,069, with 574,064 user edges.
The number of items in the Epinions dataset is 411, with 1,408 item edges.

Цитаты

"The rapid growth of online social networks has sparked substantial interest among researchers in understanding the dynamics of information dissemination within these networks."
"Real-world online social networks typically involve the simultaneous dissemination of multiple information items, with heterogeneous diffusion patterns over these items. Additionally, interconnected information items often result in multiplex influence, further complicating the dynamics of information spread."

Ключевые выводы из

Graph Bayesian Optimization for Multiplex Influence Maximization

by Zirui Yuan,M... в arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.18866.pdf

Дополнительные вопросы

How can the proposed GBIM framework be extended to handle dynamic changes in the social network and information items over time

To extend the GBIM framework to handle dynamic changes in the social network and information items over time, several strategies can be implemented. Firstly, incorporating a mechanism for real-time data updates and retraining of the surrogate model can ensure that the framework adapts to the evolving network dynamics. This involves continuously collecting new data, updating the model parameters, and reevaluating the seed set selection based on the most recent information. Additionally, implementing a feedback loop that monitors the performance of the selected seed sets over time can provide insights into the effectiveness of the chosen influencers and allow for adjustments based on the changing network conditions. Furthermore, integrating techniques from online learning and reinforcement learning can enable the framework to learn and optimize in real-time, making it more responsive to dynamic changes in the social network and information items.

What are the potential applications of the Multi-IM problem and the GBIM framework beyond social network influence maximization

The Multi-IM problem and the GBIM framework have a wide range of potential applications beyond social network influence maximization. One key application is in targeted marketing and advertising, where companies can use the framework to identify the most influential users and information items to promote their products or services effectively. This can lead to higher conversion rates and increased brand awareness. Another application is in the field of public health, where the framework can be used to identify key individuals and information items for spreading awareness about health campaigns, vaccination drives, or disease prevention strategies. Additionally, in the realm of content recommendation systems, the Multi-IM problem and GBIM framework can be utilized to enhance personalized recommendations by considering the multiplex influence of different content items on users, leading to more engaging and relevant content suggestions.

How can the information association mechanism be further improved to better capture the complex relationships between different information items

To improve the information association mechanism and better capture the complex relationships between different information items, several enhancements can be considered. Firstly, incorporating semantic analysis and natural language processing techniques can help in identifying and understanding the underlying connections between information items based on their content and context. This can enable the framework to model more nuanced associations and interactions between items, leading to more accurate influence predictions. Additionally, leveraging graph embedding methods to represent the information items and their relationships in a low-dimensional space can facilitate the modeling of complex information networks and improve the association mechanism's performance. Furthermore, integrating domain-specific knowledge and domain experts' insights can provide valuable input for refining the association mechanism and capturing the intricate relationships between diverse information items more effectively.