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Second Project for Network Science Course

This repository contains the code mentioned in the report which can be found here.

Dependencies

  • bctpy: $pip install bctpy
  • cdlib: $pip install cdlib
  • scipy: $pip install scipy
  • pandas: $pip install pandas
  • matplotlib: $pip install matplotlib

Tested in python 3.7

Organization

/BrainConnectivityToolboxWrapper/

  • Contains code to compute multiple metrics.
  • More information on its own README file.

/data/pre-processed/

  • The original data

/data/processed/

  • Data after some transformations for our usage (file process.py). Includes edge list and matrices.
  • Also includes some templates:
    • lobes.node: division of each lobe by color.
    • communities_template.node: coordinates of each of the 68 nodes.
    • template.node: template based on the Desikan atlas.

/DesikanViz/

  • Populates the Desikan template for visualization.

/reports/

  • Includes code to generate bar charts, box plots and scatter plots (plots.py).

Remaining folders are for saving the analysis results and statistics.

How to use

Run: $python main.py from root directory.

Parameters of function analyse may be changed, to select which metrics to run:

    degree_centrality = True/False
    node_strength = True/False,
    clustering_coeff = True/False,
    weighted_clustering_coeff = True/False,
    average_path_len = True/False,
    betweenness_centrality = True/False,
    newman_modularity = True/False,
    edge_betweenness = True/False,
    rich_club = True/False,
    participation_coefficient = True/False,
    communities_algorithm=
        'all'/'louvain'/'spinglass'/'walktrap'/'girvan_newman'/'infomap'
    desikan_metric=
        set this parameter with a node metric. (stats file header column)

Authors

  • João Coelho
  • João Saraiva