Influence maximization in multilayer networks based on adaptive coupling degree DOI

Su-Su Zhang,

Ming Xie, Chuang Liu

et al.

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2025, Volume and Issue: 35(3)

Published: March 1, 2025

Influence maximization (IM) aims to identify highly influential nodes maximize influence spread in a network. Previous research on the IM problem has mainly concentrated single-layer networks, disregarding comprehension of coupling structure that is inherent multilayer networks. To solve we first propose an independent cascade model (MIC) network where propagation occurs simultaneously across different layers. Consequently, heuristic algorithm, i.e., adaptive degree (ACD), which selects seed with high and low overlap influence, proposed for By conducting experiments based MIC, have demonstrated our method superior baselines terms time cost six synthetic four real-world

Language: Английский

Influence maximization in multilayer networks based on adaptive coupling degree DOI

Su-Su Zhang,

Ming Xie, Chuang Liu

et al.

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2025, Volume and Issue: 35(3)

Published: March 1, 2025

Influence maximization (IM) aims to identify highly influential nodes maximize influence spread in a network. Previous research on the IM problem has mainly concentrated single-layer networks, disregarding comprehension of coupling structure that is inherent multilayer networks. To solve we first propose an independent cascade model (MIC) network where propagation occurs simultaneously across different layers. Consequently, heuristic algorithm, i.e., adaptive degree (ACD), which selects seed with high and low overlap influence, proposed for By conducting experiments based MIC, have demonstrated our method superior baselines terms time cost six synthetic four real-world

Language: Английский

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