GitHub - MaximilianPavon/DM_project: Programming project for the course 'Algorithmic Methods of Data Mining' in the masters programme 'Machine Learning, Data Science and Artificial Intelligence' at Aalto University

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Programming project for the course 'Algorithmic Methods of Data Mining' in the masters programme 'Machine Learning, Data Science and Artificial Intelligence' at Aalto University

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Name		Name	Last commit message	Last commit date
Latest commit History 135 Commits
data		data
figures		figures
graphs		graphs
old files		old files
.gitignore		.gitignore
README.txt		README.txt
analysis.py		analysis.py
analysis_nx.py		analysis_nx.py
docker with graph tools.sh		docker with graph tools.sh
gt_2_2_approx_vs_accuracy_wiki.ipynb		gt_2_2_approx_vs_accuracy_wiki.ipynb
gt_approx_soc-epi.ipynb		gt_approx_soc-epi.ipynb
gt_approx_wiki.ipynb		gt_approx_wiki.ipynb
gt_approx_wiki_filters-shit.ipynb		gt_approx_wiki_filters-shit.ipynb
gt_brute_2_2_approx_vs_accuracy_soc_epi.py		gt_brute_2_2_approx_vs_accuracy_soc_epi.py
gt_brute_approx_ego-gplus.py		gt_brute_approx_ego-gplus.py
gt_brute_approx_soc-Epinions1.py		gt_brute_approx_soc-Epinions1.py
gt_brute_approx_wiki.py		gt_brute_approx_wiki.py
gt_brute_exact_soc-Epinions1.py		gt_brute_exact_soc-Epinions1.py
gt_brute_exact_wiki.py		gt_brute_exact_wiki.py
gt_create_ adjmatrix.ipynb		gt_create_ adjmatrix.ipynb
gt_exact_stats_ego-Gplus.ipynb		gt_exact_stats_ego-Gplus.ipynb
gt_exact_stats_soc_ep.ipynb		gt_exact_stats_soc_ep.ipynb
gt_exact_stats_wiki.ipynb		gt_exact_stats_wiki.ipynb
jupyter_on_brute.txt		jupyter_on_brute.txt
nx_2_plot_epi.ipynb		nx_2_plot_epi.ipynb
nx_2_plot_wiki.ipynb		nx_2_plot_wiki.ipynb
nx_approx_methods.py		nx_approx_methods.py
nx_approx_wiki.ipynb		nx_approx_wiki.ipynb
nx_brute_ego-Gplus.py		nx_brute_ego-Gplus.py
nx_brute_soc-Epinions1.py		nx_brute_soc-Epinions1.py
nx_brute_wiki.py		nx_brute_wiki.py
nx_exact_stats_soc-Epinions1.ipynb		nx_exact_stats_soc-Epinions1.ipynb
nx_exact_stats_wiki_vote.ipynb		nx_exact_stats_wiki_vote.ipynb
project.pdf		project.pdf
refs.bib		refs.bib
report.tex		report.tex

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README file for: Programming project CS-E4600 - Algorithmic Methods of Data Mining

authors: Héctor Laria Mantecón and Maximilian Proll

Packages:

This project has been programmed using Graph-Tool and NetworkX Python libraries.
Data exploration has been performed using Jupyter Notebooks.
Both NetworkX and Jupyter Notebook are common and can be obtained from an Anaconda
installation or installed by PIP. For installing Graph-Tool however, we recommend
using its docker container instead because of the huge amount of dependencies and
the problems installing some of them.
More information here https://git.skewed.de/count0/graph-tool/wikis/installation-instructions#installing-using-docker

Nomenclatura for files:

prefix:
gt_* 			- file written using library graph-tool
nx_*			- file written using library networkX

middlefix:
*_exact_stats_*		- compute exact statistics
*_approx_*		- compute approximate statistics with random sampling of pairs of vertices
*_brute_*		- indicates that this code has been used to run on brute/force

suffix:
indicates which network has been used
*_wiki_*   				- WikiVote network
*_soc-Epinions1_* *_soc_epi_* *_epi_*	- Epinions network

file ending:
*.py			- Python file
*.ipynb			- Jupyter Notebook

analysis.py and analysis_nx.py define functions in Graph-Tool and NetworkX respectively

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Programming project for the course 'Algorithmic Methods of Data Mining' in the masters programme 'Machine Learning, Data Science and Artificial Intelligence' at Aalto University

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