Link strength diffusion model for influence diffusion in weighted social networks

Diffusion models are critical for spreading influence in social networks, as they contribute in estimating the potential reach of seed nodes, allowing for better evaluation of their goodness and effectiveness. This research aims to develop a diffusion model for weighted social networks, addressing a gap in traditional models that typically only consider the binary state of relationships between nodes. While existing models treat edges as simply existing or not, real-world social networks are inherently weighted, with edge weights reflecting the strength of the link between connecting nodes. To better capture these dynamics, we propose the Link Strength Diffusion (LiSt-D) model, which incorporates the strength of shared relation to determine the probability of diffusion between social actors. The LiSt-D model was tested on three real-world weighted social networks, Bitcoin Alpha, Bitcoin OTC, and Advogato, using seven heuristics to select the initial seed nodes. The findings showed that diffusion spread under LiSt-D covered 90% of the Bitcoin Alpha network, 84% of Bitcoin OTC, and 63% of the Advogato network. LiSt-D model captures the heterogeneity in the link-strength connecting nodes, and demonstrates early peak performance with high stability.