## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, warning = FALSE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(clustAnalytics) ## ----------------------------------------------------------------------------- data(karate, package="igraphdata") scoring_functions(karate, V(karate)$Faction, type="local") scoring_functions(karate, V(karate)$Faction, type="global") ## ----------------------------------------------------------------------------- cut_ratio(karate, V(karate)$Faction) ## ----------------------------------------------------------------------------- data(karate, package="igraphdata") E(karate) rewired_karate <- rewireCpp(karate, weight_sel = "max_weight") E(rewired_karate) ## ----------------------------------------------------------------------------- data(foodwebs, package="igraphdata") rewired_ChesLower <- rewireCpp(foodwebs$ChesLower, weight_sel = "max_weight") ## ----------------------------------------------------------------------------- # this corresponds to the club each member ended up with after the split, # which we could consider the ground truth clustering for this graph. significance_table_karate <- evaluate_significance(karate, alg_list=list(Louvain=cluster_louvain, "label prop"= cluster_label_prop, walktrap=cluster_walktrap), gt_clustering=V(karate)$Faction) significance_table_karate ## ----------------------------------------------------------------------------- significance_table_karate_r <- evaluate_significance_r(karate, alg_list=list(Louvain=cluster_louvain, "label prop"= cluster_label_prop, walktrap=cluster_walktrap), gt_clustering=V(karate)$Faction, weight_sel="max_weight", n_reps=10) ## ----------------------------------------------------------------------------- pm <- matrix (c(.3, .001, .001, .003, .001, .2, .005, .002, .001, .005, .2, .001, .003, .002, .001, .3), nrow=4, ncol=4) g_sbm <- sample_sbm(100, pref.matrix=pm, block.sizes=c(25,25,25,25)) E(g_sbm)$weight <- 1 significance_table_sbm <- evaluate_significance(g_sbm) significance_table_sbm ## ----------------------------------------------------------------------------- data(package="clustAnalytics",g_forex) significance_table_karate_r <- evaluate_significance_r(karate, gt_clustering=V(karate)$Faction, weight_sel = "const_var", n_reps=5, w_max=1) ## ----------------------------------------------------------------------------- b_karate <- boot_alg_list(g=karate, return_data=FALSE, R=99) b_karate ## ----------------------------------------------------------------------------- b_sbm <- boot_alg_list(g=g_sbm, return_data=FALSE, R=99) b_sbm