IEEE Computational Intelligence Magazine - February 2023 - 52
A Multi-Transformation
Evolutionary Framework for
Influence Maximization in
Social Networks
Chao Wang,Jiaxuan Zhao, Lingling Li,
LichengJiao ,Jing Liu, and Kai Wu
Xidian University, CHINA
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
Digital Object Identifier 10.1109/MCI.2022.3222050
Date ofcurrent version: 13January 2023
52 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2023
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.
Corresponding author: Licheng Jiao (e-mail: lchjiao@mail.xidian.edu.cn).
1556-603X ß 2023 IEEE
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IEEE Computational Intelligence Magazine - February 2023
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