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Implementation of Network Analysis using Markov Chains in Python
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 75 |
| Year of Publication: 2026 |
| Authors: Ahmad Farhan AlShammari |
10.5120/ijca2026926300
|
Ahmad Farhan AlShammari . Implementation of Network Analysis using Markov Chains in Python. International Journal of Computer Applications. 187, 75 ( Jan 2026), 86-93. DOI=10.5120/ijca2026926300
Abstract
The goal of this research is to implement network analysis using Markov chains in Python. Networks exist almost everywhere in life. There are networks of computers, people, articles, posts, etc. Network analysis is used to understand the structure, function, and performance of the network. Markov chains method is used to predict the future state based on the present state and not on the previous states. The basic steps of network analysis using Markov chains are explained: defining network (states, transition matrix, and distribution vector), performing matrix multiplication (computing stationary distribution vector and computing stationary transition vector), performing random walk (computing stationary distribution vector), comparing results, and plotting charts. The developed program was tested on an experimental data. The program has successfully performed the basic steps of network analysis using Markov chains and provided the required results.
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- Python: http://www.python.org
- Numpy: http://www.numpy.org
- Pandas: http://pandas.pydata.org
- Matplotlib: http://www. matplotlib.org
- Seaborn: http://seaborn.pydata.org
- SciPy: http://scipy.org
- NLTK: http://www.nltk.org
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