https://github.com/lopezjurip/k-walk-py

Markov chain theory applied on finding most frequently visited edges from random walks
https://github.com/lopezjurip/k-walk-py

Last synced: 25 days ago
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Markov chain theory applied on finding most frequently visited edges from random walks

• Host: GitHub
• URL: https://github.com/lopezjurip/k-walk-py
• Owner: lopezjurip
• License: mit
• Created: 2015-05-21T01:56:22.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2015-05-21T02:57:31.000Z (over 9 years ago)
• Last Synced: 2024-03-15T14:33:11.061Z (8 months ago)
• Language: Python
• Homepage: http://becool.info.ucl.ac.be/pub/papers/rr2006-07_walks.pdf
• Size: 133 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# k-walk-py

Markov chain theory applied on finding most frequently visited edges from random walks. Based on paper *Relevant subgraph extraction from random

walks in a graph (2006)* from P. Dupont, J. Callut, G. Dooms, J.-N. Monette and Y. Deville.

# Instructions

* Install [numpy](https://github.com/numpy/numpy): `pip install numpy`.

* Modify `main.py` and create your nodes (`V`), edges (`E`) and points of interest (`K`). 

* Create an instance of `Graph` with those parameters.

* Query `N_star` function with a starting point of interest.

* Print the resulting matrix to see how frecuently is visited each node.