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Dynamic linear threshold model with decay for influence spread
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  • Xunlian Wu ,
  • Anqi Zhang,
  • Yining Quan ,
  • Qiguang Miao ,
  • Peng Gang Sun
Xunlian Wu

Corresponding Author:[email protected]

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Anqi Zhang
Yining Quan
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Qiguang Miao
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Peng Gang Sun
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Influence spread within social networks exerts a profound impact on individuals’ perceptions, opinions, and behaviors. The complexity of influence spread poses significant challenges to its accurate modeling. Traditional approaches, such as the Linear Threshold (LT) model, offer a simplified depiction of this phenomenon but overlook the temporal decay of influence. To address this limitation, we introduce a Dynamic Linear Threshold Model with Decay (DLTD), incorporating three distinct decay strategies: random decay, linear decay, and nonlinear decay. Comparative analyses on real-world datasets reveal that DLTD achieves superior accuracy over the LT model, particularly when employing linear decay. Notably, DLTD demonstrates enhanced alignment with ground-truth spread as the initial seed set expands and exhibits greater resilience to variations in network structure. Furthermore, an interesting finding indicates that the balance between community structures’ internal links and external links is more effective for influence spread.
20 Mar 2024Submitted to TechRxiv
29 Mar 2024Published in TechRxiv