A novel evolutionary deep reinforcement learning algorithm for the influence maximization problem in multilayer social networks

Jianxin Tang, Chenshuo Li, Lijun Liu, Tianpeng Xu and Yabing Yao

Chaos, Solitons & Fractals, 2025, vol. 200, issue P1

Abstract: How to identify a set of influential individuals that can ensure the most information diffusion in multilayer social networks remains a fundamental yet underexplored issue of the influence maximization problem. Existing solutions mostly simplify or even neglect the heterogeneous characteristics of individuals from different layers, and the inter-layer propagation dynamics of the information spreading in the multilayer social networks. To address such challenges, a cross-layer independent cascade model is proposed to capture the inter-layer information cascading effect. Furthermore, this paper proposes a differential evolution-aided deep reinforcement learning (DEDRL) algorithm to identify the optimal seed set for the influence maximization in multilayer networks. More specifically, a multilayer network embedding mechanism is conceived to learn node embeddings of multilayer networks and the differential evolution is integrated with deep reinforcement learning to evolve a population composed of deep Q network weight parameters. Experimental evaluations conducted on both synthetic and real-world multilayer networks demonstrate the effectiveness of the proposed DEDRL and show an average performance improvement of 3.8% compared to the state-of-the-art algorithms.

Keywords: Multilayer social networks; Influence maximization; Cross-layer independent cascade model; Multilayer network embedding; Differential evolution; Deep reinforcement learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:200:y:2025:i:p1:s0960077925009804

DOI: 10.1016/j.chaos.2025.116967

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