Adding experiments that look at the results from the community detection:
1) Using Omega score as a distance metric cluster the distance matrix produced by run_comparison.py. This clustering is an attempt and finding how similar various algorithms are.
See: Issue #18 for additional Info
2) Create historgrams of metrics across algorithms. This attempts to show how the communities differ across the various algorithms
3) Update the Omega Score vs. Time plot for readability
Adding experiments that look at the results from the community detection: 1) Using Omega score as a distance metric cluster the distance matrix produced by run_comparison.py. This clustering is an attempt and finding how similar various algorithms are. See: Issue #18 for additional Info 2) Create historgrams of metrics across algorithms. This attempts to show how the communities differ across the various algorithms
3) Update the Omega Score vs. Time plot for readability