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Implementation of Network Ranking using PageRank and Random Walk in Python
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 83 |
| Year of Publication: 2026 |
| Authors: Ahmad Farhan AlShammari |
10.5120/ijca2026926463
|
Ahmad Farhan AlShammari . Implementation of Network Ranking using PageRank and Random Walk in Python. International Journal of Computer Applications. 187, 83 ( Feb 2026), 40-47. DOI=10.5120/ijca2026926463
Abstract
The goal of this research is to implement network ranking using PageRank and random walk in Python. Network ranking is used to calculate the importance of nodes in the network. It helps to order the nodes according to their ranking values. Network ranking is performed using PageRank and random walk. The final results are compared to make sure that the two methods are matching. The basic steps of network ranking using PageRank and random walk are explained: defining network (nodes, adjacency matrix, and rank vector), computing outgoing nodes, computing transition matrix, performing PageRank, performing random walk, comparing results, and plotting charts. The developed program was tested on an experimental data. The program has successfully performed the basic steps of network ranking using PageRank and random walk and provided the required results.
References
- Sammut, C., & Webb, G. I. (2011). "Encyclopedia of Machine Learning". Springer.
- Jung, A. (2022). "Machine Learning: The Basics". Springer.
- Kubat, M. (2021). "An Introduction to Machine Learning". Springer.
- Li, H. (2023). "Machine Learning Methods". Springer.
- Zollanvari, A. (2023). " Machine Learning with Python". Springer.
- Chopra, D., & Khurana, R. (2023). "Introduction to Machine Learning with Python". Bentham Science Publishers.
- Müller, A. C., & Guido, S. (2016). "Introduction to Machine Learning with Python: A Guide for Data Scientists". O'Reilly Media.
- Raschka, S. (2015). "Python Machine Learning". Packt Publishing.
- Forsyth, D. (2019). "Applied Machine Learning". Springer.
- Sarkar, D., Bali, R., & Sharma, T. (2018). "Practical Machine Learning with Python". Apress.
- Igual, L., & Seguí, S. (2017). "Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications". Springer.
- VanderPlas, J. (2017). "Python Data Science Handbook: Essential Tools for Working with Data". O'Reilly Media.
- Muddana, A., & Vinayakam, S. (2024). "Python for Data Science". Springer.
- Unpingco, J. (2021). "Python Programming for Data Analysis". Springer.
- Zelle, J. (2017). "Python Programming: An Introduction to Computer Science". Franklin, Beedle & Associates.
- Xanthidis, D., Manolas, C., Xanthidou, O. K., & Wang, H. I. (2022). "Handbook of Computer Programming with Python". CRC Press.
- Chun, W. (2001). "Core Python Programming". Prentice Hall Professional.
- Padmanabhan, T. (2016). "Programming with Python". Springer.
- Beazley, D., & Jones, B. K. (2013). "Python Cookbook: Recipes for Mastering Python 3". O'Reilly Media.
- Newman, M. (2018). "Networks: An Introduction". Oxford University Press.
- Estrada, E. & Knight, P. (2015). "A First Course in Network Theory". Oxford University Press.
- Menczer, F., Fortunato, S., & Davis, C. A. (2020). "A First Course in Network Science". Cambridge University Press.
- Barabasi, A. (2016). "Network Science". Cambridge University Press
- Lewis, T. (2009). "Network Science: Theory and Applications". John Wiley & Sons.
- Knickerbocker, D. (2023). "Network Science with Python". Packt Publishing.
- Norris, J. (2009). "Markov Chains". Cambridge University Press.
- Tolver, A. (2016). "An Introduction to Markov Chains". Department of Mathematical Sciences, University of Copenhagen.
- Weber, R. (2011). "Markov Chains". Department of Pure Mathematics and Mathematical Statistics. University of Cambridge.
- Ching, W., Huang, S., Ng, M., & Siu, T. (2013). "Markov Chains: Models, Algorithms, and Applications". Springer.
- Privault, N. (2018). "Understanding Markov Chains: Examples and Applications". Springer.
- Ankan, A. & Panda, A. (2018). "Hands-On Markov Models with Python". Packt Publishing.
- Brin, S., & Page, L. (1998). "The Anatomy of a Large-Scale Hypertextual Web Search Engine". Computer Networks and ISDN Systems, 30(1-7), 107-117.
- Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). "The PageRank Citation Ranking: Bringing Order to The Web". Stanford University.
- Langville, A., & Meyer, C. (2011). "Google's PageRank and Beyond: The Science of Search Engine Rankings". Princeton University Press.
- Google: http://www.google.com
- Grinstead, C., & Snell, J. (1997). "Introduction to Probability". American Mathematical Society.
- 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
- SK Learn: http://scikit-learn.org
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