# MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization

arXiv: https://arxiv.org/abs/2402.16898 · HTML: https://mlsanity.com/paper/2402.16898/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 24 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)
- [Layer aggregation and reducibility of multilayer interconnected networks](https://mlsanity.com/paper/1405.0425/) (2014)
- [Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency](https://mlsanity.com/paper/1404.0900/) (2014)
- [Deep Graph Representation Learning and Optimization for Influence Maximization](https://mlsanity.com/paper/2305.02200/) (2023)
- [Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems](https://mlsanity.com/paper/2206.12327/) (2022)
- [Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks](https://mlsanity.com/paper/1605.07990/) (2016)
- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [A Survey on Influence Maximization in a Social Network](https://mlsanity.com/paper/1808.05502/) (2018)
- [ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning](https://mlsanity.com/paper/2210.07500/) (2022)
- [DeepGAR: Deep Graph Learning for Analogical Reasoning](https://mlsanity.com/paper/2211.10821/) (2022)
- [Unraveling the Origin of Social Bursts in Collective Attention](https://mlsanity.com/paper/1903.06588/) (2019)
- [Least Cost Influence Maximization Across Multiple Social Networks](https://mlsanity.com/paper/1606.08927/) (2016)
- [Multiplex Influence Maximization in Online Social Networks with Heterogeneous Diffusion Models](https://mlsanity.com/paper/1802.01729/) (2018)

## Built on by (most cited first, 1 of 1)

- [Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models](https://mlsanity.com/paper/2503.06413/) (2025)

## Abstract (arXiv)

Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network. MIM has been one of central research topics, especially in nowadays social networking landscape where users participate in multiple online social networks (OSNs) and their influences can propagate among several OSNs simultaneously. Although there exist a couple combinatorial algorithms to MIM, learning-based solutions have been desired due to its generalization ability to heterogeneous networks and their diversified propagation characteristics. In this paper, we introduce MIM-Reasoner, coupling reinforcement learning with probabilistic graphical model, which effectively captures the complex propagation process within and between layers of a given multiplex network, thereby tackling the most challenging problem in MIM. We establish a theoretical guarantee for MIM-Reasoner as well as conduct extensive analyses on both synthetic and real-world datasets to validate our MIM-Reasoner's performance.