Graph metrics #106
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Graph metrics #106
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Computes average connectivity using minimum edge cuts from a weighted/unweighted sample size of vertex pairs
Added minimal comments documenting the code
Uses a sample to approximate a graph's average eccentricity and the diameter of the graph (maximum eccentricity in the sample)
Add flush=True to print statements and add intermediate print statements to show progress
Add flush=True to print statements and add intermediate print statements to track progress
Computes the average cluster coefficient of a graph using squares
Shuffle vertices before processing
Converts a visits.csv file to a sparse adjacency matrix form (compressed sparse row)
Utilized pandas and scipy.sparse optimizations to heavily optimize this conversion
Add printing of metrics to the end (number of non zeroes, average number of non zeroes per row, 1-norm, and frobenius norm)
Takes in original population directory, proportion of visits to change, and output population directory. Randomly replaces that proportion of visits' (without replacement) with locations that are randomly picked (with replacement). Then, the new population directory is created with the new visits.csv file and soft links to other population files in the old directory.
…ph_perturbation.py to point to
…s to maintain types in input file
Script that calculate and print all of the relevant sparse adjacency matrix metrics as well as location heuristics for a given population directory
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