diff --git a/README.md b/README.md index 52a09e0df89852d188fcb7c58d9bd46a0249f499..29ac8d449150be79e1583d7b291119b63ae7e068 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,34 @@ # DSS -Comparision of several quasi-clique mining algorithms and their application to the DSS method. \ No newline at end of file +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) +