Multilevel Graph Clustering with Density-Based Quality Measures

4 Evaluationparameter component description+values defaultcoarsening method coarsening method to merge cluster pairs;greedy grouping, greedy matchingreduction factor coarsening number of clusters to merge ineach coarsening level; 5%–50%match fraction coarsening number of best ranked pairsto consider in matching; 50%–100%merge selector coarsening ranking of cluster pairs;modularity increase, weight density,RW distance, RW reachability,spectral length, spectrallength difference, spectral anglegreedy grouping10%50%weight densityRW steps selector length of random walks 2RW iterations selector iterative applications of reachability3spectral ratio selector number of eigenvectors to use,20%cut off value for λ j /λ 1spectral max ev selector maximal number of eigenvectors 30refinement method refinement method to move vertices;complete greedy, sorted greedy,Kernighan-Linvertex selector refinement ranking of vertices insorted greedy refinement;mod-fitness, eo-fitness, densityfitnesssearch depth refinement when to abort inner-loop searchin Kernighan-Lin, multiplied bylog 10 |V |sorted greedydensity fitness25Table 4.1: The Configuration Space60