# A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks

arXiv: https://arxiv.org/abs/2204.03297 · HTML: https://mlsanity.com/paper/2204.03297/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 67 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Evolutionary Multitasking for Single-objective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results](https://mlsanity.com/paper/1706.03470/) (2017)
- [A Survey on Influence Maximization in a Social Network](https://mlsanity.com/paper/1808.05502/) (2018)
- [Controlling Elections through Social Influence](https://mlsanity.com/paper/1711.08615/) (2017)
- [NetworkRepository: An Interactive Data Repository with Multi-scale Visual Analytics](https://mlsanity.com/paper/1410.3560/) (2014)

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

- [Automatic Graph Topology-Aware Transformer](https://mlsanity.com/paper/2405.19779/) (2024)
- [Knowledge-aware Evolutionary Graph Neural Architecture Search](https://mlsanity.com/paper/2411.17339/) (2024)
- [Large-scale multi-objective influence maximisation with network downscaling](https://mlsanity.com/paper/2204.06250/) (2022)
- [Structure-Aware Cooperative Ensemble Evolutionary Optimization on Combinatorial Problems with Multimodal Large Language Models](https://mlsanity.com/paper/2510.21906/) (2025)

## Abstract (arXiv)

Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluate the influence spread of a seed set efficiently, existing studies have proposed transformations with lower computational costs to replace the expensive Monte Carlo simulation process. These alternate transformations, based on network prior knowledge, induce different search behaviors with similar characteristics to various perspectives. Specifically, it is difficult for users to determine a suitable transformation a priori. This article proposes a multi-transformation evolutionary framework for influence maximization (MTEFIM) with convergence guarantees to exploit the potential similarities and unique advantages of alternate transformations and to avoid users manually determining the most suitable one. In MTEFIM, multiple transformations are optimized simultaneously as multiple tasks. Each transformation is assigned an evolutionary solver. Three major components of MTEFIM are conducted via: 1) estimating the potential relationship across transformations based on the degree of overlap across individuals of different populations, 2) transferring individuals across populations adaptively according to the inter-transformation relationship, and 3) selecting the final output seed set containing all the transformation's knowledge. The effectiveness of MTEFIM is validated on both benchmarks and real-world social networks. The experimental results show that MTEFIM can efficiently utilize the potentially transferable knowledge across multiple transformations to achieve highly competitive performance compared to several popular IM-specific methods. The implementation of MTEFIM can be accessed at https://github.com/xiaofangxd/MTEFIM.