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 # DSS
 
-Comparision of several quasi-clique mining algorithms and their application to the DSS method.
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+Comparision of several quasi-clique mining algorithms and their application to the DSS method. This code uses the modules networkx, random, time and matplotlib.pyplot.
+
+The different quasi-clique mining algorithms used are:
+
+    - Quick (Effective Pruning Techniques for Mining Quasi-Cliques 2008) implemented in the file quick.py
+
+    - Quick_redundancy_aware (same as before and adding a method from Redundancy Aware Maximal Cliques 2013) implemented in the file quick_red_aware.py
+
+    - Quick_delete_covered_edges, implemented in the file quick_del_edges.py
+
+    - A greedy quasi-clique mining algorithm implemented in greedy_qclq_mining.py
+
+These algorithms are compared trough several aspects (runtime/compression rates/visibility), each time with and without the pre-processing algorithm (the reduction procedure of tools.py).
+
+Three types of graph have been used:
+    - community graphs (generated with the file community_graph.py)
+
+    - LFR graphs (the dataset used is available here: https://zenodo.org/record/4450167?fbclid=IwAR1abb2_3qotisogJW9i_F_6RL-Cjd_czgljmh-tjONXOOzgMVfHnLWx0Ac#.Yi4FcDXjK3C and the setB folder must be placed in graph_database/LFR_graphs)
+
+    - social networks (the dataset used is available here: https://snap.stanford.edu/data/ and must be placed in graph_database/Social_networks)
+
+To compare the algorithms run either (can take several hours):
+    - test_community_graphs.py
+    - test_LFR_graphs.py
+    - test_social_networks.py
+The results will be saved in the results folder.
+
+To output the results run either:
+    - results_community_graphs.py (or visibility_community_graphs.py)
+    - results_LFR_graphs.py (or visibility_LFR_graphs.py)
+    - results_social_networks.py (or visibility_social_networks_graphs.py)
+