A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks
Chao Wang, Jiaxuan Zhao, Lingling Li, Licheng Jiao, Jing Liu, Kai Wu

TL;DR
This paper introduces a multi-transformation evolutionary framework for influence maximization in social networks, which optimizes multiple influence transformations simultaneously to improve efficiency and effectiveness without requiring manual selection of transformations.
Contribution
It proposes a novel multi-transformation evolutionary framework with convergence guarantees that leverages relationships among transformations to enhance influence maximization performance.
Findings
Achieves highly competitive influence spread performance.
Efficiently utilizes transferable knowledge across transformations.
Validated on benchmarks and real-world social networks.
Abstract
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…
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Taxonomy
TopicsDigital Marketing and Social Media
