Detailed-level modelling of influence spreading on complex networks
Vesa Kuikka1, Kimmo K Kaski2,3
1Department of Computer Science, Aalto University School of Science, P.O. Box 15500, 00076, Aalto, Finland. vesa.kuikka@aalto.fi.
Scientific Reports
|November 14, 2024
Summary
We developed a new computational model for analyzing influence spreading across complex networks. This probability matrix approach enhances understanding of network dynamics in social, community, and epidemic spread scenarios.
Area of Science:
- Computational modeling
- Network science
- Complex systems analysis
Background:
- High-performance computing enables detailed modeling of spreading processes.
- Existing studies lack detailed computational methods for large complex networks.
Purpose of the Study:
- To present a novel modeling approach for analyzing influence spreading on complex networks.
- To provide detailed computational methods for understanding node and link influence.
Main Methods:
- Developed an influence-spreading model using a probability matrix to quantify node-to-node transmission.
- Integrated sub-models for diverse applications including social networks, community detection, and epidemic spreading.
- Utilized centrality measures based on the probability matrix to identify significant network nodes.
Main Results:
- The model effectively analyzes network characteristics and spreading processes using consistent metrics.
- Demonstrated applicability across social networks, community detection, and epidemic modeling.
- Identified key nodes through probability matrix-based centrality measures.
Conclusions:
- The proposed model offers a comprehensive framework for analyzing influence spreading in complex networks.
- The approach is adaptable for future extensions, including node breakthrough probabilities and link temporal dynamics.
- Provides a robust method for understanding network behavior and identifying critical components.
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