An improved clustering based multi-objective evolutionary algorithm for influence maximization under variable-length solutions

作者：

Highlights：

• Formulates a generic version of Multi-objective IM Problem (MIMP).

• Proposes an improved θDEA algorithm, named as θDEA-II to solve the MIMP.

• It includes a novel gap-based selection operator to promote diversity.

• Uses a different θ-dominance relationship and normalization technique.

• A detailed theoretical analysis indicates some changes to current practice.

摘要

•Formulates a generic version of Multi-objective IM Problem (MIMP).•Proposes an improved θDEA algorithm, named as θDEA-II to solve the MIMP.•It includes a novel gap-based selection operator to promote diversity.•Uses a different θ-dominance relationship and normalization technique.•A detailed theoretical analysis indicates some changes to current practice.

论文关键词：Influence maximization,Multi-objective evolutionary algorithm,Variable-length solutions,θ-dominance,Social network analysis

论文评审过程：Received 6 May 2022, Revised 22 August 2022, Accepted 31 August 2022, Available online 5 September 2022, Version of Record 16 September 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.109856