Modeling Graph Dynamics with Semi-Markov Processes
Author Information
Author(s): Marco Raberto, Fabio Rapallo, Enrico Scalas
Hypothesis
Can a model of graph dynamics be effectively created using a Markov chain and a semi-Markov counting process?
Conclusion
The study presents a model that captures the dynamics of graphs, particularly in social networks, by using a combination of Markov chains and semi-Markov processes.
Supporting Evidence
- The model incorporates both discrete and continuous time evolution of graphs.
- It highlights the volatility of social networks and their dynamic nature.
- The study uses simulations to demonstrate the behavior of the proposed model.
Takeaway
This study shows how we can use math to understand how connections in networks change over time, like how friends might come and go.
Methodology
The model combines a Markov chain on undirected graphs with a semi-Markov counting process to analyze graph dynamics.
Limitations
The model primarily focuses on undirected graphs and may not fully capture the complexities of directed or weighted graphs.
Digital Object Identifier (DOI)
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