Machine learningNetwork science

Directed Network Diffusion Analysis

Directed network diffusion analysis studies how information, disease, behavior, or influence spreads through a network in which edges carry direction — meaning transmission flows one way along each link. It combines graph-theoretic representations with stochastic spreading models such as independent cascade, linear threshold, or SIR/SIS, and is central to influence maximization, epidemic forecasting, and information propagation research.

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Sources

  1. Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 137–146. DOI: 10.1145/956750.956769
  2. Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015). Epidemic processes in complex networks. Reviews of Modern Physics, 87(3), 925–979. DOI: 10.1103/RevModPhys.87.925

Related methods

ScholarGateDirected Network Diffusion Analysis (Directed Network Diffusion Analysis (Influence and Spreading Processes on Directed Graphs)). Retrieved 2026-06-04 from https://scholargate.app/en/network-analysis/directed-network-diffusion-analysis