updated post scripts

Author: vlabatut

src/post/description/_all_post.R

@@ -22,9 +22,9 @@ source("src/post/description/comp_vs_edges.R")
 source("src/post/description/comp_vs_vertices.R")
 source("src/post/description/deg_plots.R")
 source("src/post/description/diameters.R")
-#source("src/post/description/nei_vs_deg.R")
-#source("src/post/description/partial_extr.R")
-#source("src/post/description/pref_attach.R")
+source("src/post/description/nei_vs_deg.R")
+source("src/post/description/partial_extr.R")
+source("src/post/description/pref_attach.R")
 #source("src/post/description/robustness.R")
 #source("src/post/description/sex_stats.R")
 #source("src/post/description/trans_vs_deg.R")

src/post/description/nei_vs_deg.R

@@ -21,24 +21,29 @@ start.rec.log(text="NeighVsDeg")
 
 # read the graph
 graph.file <- get.path.data.graph(mode="scenes", net.type="static", filtered=FALSE, pref="graph", ext=".graphml")
+tlog(0,"Read the graph file: \"",graph.file,"\"")
 g <- read.graphml.file(file=graph.file)
 
 # compute values
+tlog(2,"Compute the graph degree")
 deg.vals <- degree(graph=g, mode="all")
 tmp <- igraph::knn(graph=g, weights=NULL)#, mode="all", neighbor.degree.mode="all")
 
 # filter out zero degree and NaN
+tlog(2,"Filter out zero degree nodes")
 idx <- which(!is.nan(tmp$knn) & tmp$knn>0)
 filt.nei <- tmp$knn[idx]
 filt.deg <- deg.vals[idx]
 
 # keep tail
+tlog(2,"Keep tail")
 thresholds <- quantile(filt.deg, probs=c(0,0.25,0.50,0.75,0.85,0.90,0.95))
 threshold <- thresholds[4]	# exp=0.48
 cut.nei <- filt.nei[filt.deg>=threshold]
 cut.deg <- filt.deg[filt.deg>=threshold]
 
 # init parameters using a linear regression
+tlog(2,"Perform regression")
 fit <- lm(log(cut.nei) ~ log(cut.deg))
 summary(fit)
 params <- fit$coefficients
@@ -49,9 +54,9 @@ val3 <- 0
 # perform NL regression
 df <- data.frame(cut.deg, cut.nei)
 fit <- nlsLM(cut.nei ~ c1*cut.deg^c2, 
-		start=list(c1=val1, c2=val2),
-		data = df,
-		control=list(maxiter=200))
+	start=list(c1=val1, c2=val2),
+	data = df,
+	control=list(maxiter=200))
 summary(fit)
 
 # plot
@@ -61,7 +66,8 @@ col.sec <- combine.colors(col, "WHITE", transparency=20)
 xlab <- "Degree $k$"
 ylab <- "Neighbors' average Degree $<k_{nn}>$"
 exponent <- summary(fit)$coefficients["c2","Estimate"]
-plot.file <- get.path.stats.topo(net.type="static", mode="scenes", meas.name=MEAS_DEGREE, filtered="both", suf="nei.deg_vs_degree")
+plot.file <- get.path.stats.topo(net.type="static", mode="scenes", meas.name=MEAS_MULTI_NODES, filtered="both", suf="nei-degree_vs_degree")
+tlog(2,"Plot in \"",plot.file,"\"")
 pdf(file=paste0(plot.file,PLOT_FORMAT_PDF), bg="white")
 par(
 	mar=c(4,4,0,0)+0.1,	# remove the title space Bottom Left Top Right
@@ -130,6 +136,7 @@ legend(
 
 ###############################################################################
 # add the plot for the filtered net, as an inset
+tlog(2,"Same thing for filtered net")
 
 # filter the characters
 filt.names <- V(g)$name[V(g)$Filter=="Discard"]

src/post/description/partial_extr.R

@@ -18,8 +18,8 @@ start.rec.log(text="PartialExtr")
 
 
 ###############################################################################
-# represents the narrative as two distinct networks 
-tlog(0,"Extract two distinct networks for the narrative")
+# divides the narrative into two distinct networks 
+tlog(0,"Extract two distinct networks from the narrative")
 
 # load stats
 data <- read.corpus.data()

src/post/description/pref_attach.R

@@ -81,6 +81,7 @@ gsl[[2]] <- gs.filt
 
 
 
+
 ################################################################################
 filts <- c(FALSE, TRUE)
 modes <- c("all", "external", "internal")
@@ -107,20 +108,20 @@ ylab["internal"] <- "Attachment probability $\\kappa(k_1 k_2)$"
 
 tlog(2,"Looping over modes (all, external, internal")
 for(mode in modes)
-{	tlog(2,"Processing mode ",mode)
+{	tlog(2,"Processing mode \"",mode,"\"")
 
 	tlog(2,"Looping over unfiltered/filtered graphs")
 	for(f in 1:length(filts))
 	{	gs <- gsl[[f]]
 		if(filts[f])
-		{	filtered <- "filtered"
+		{	filt.txt <- "filtered"
 			col <- pal["Keep"]
 		}
 		else
-		{	filtered <-"unfiltered"
+		{	filt.txt <-"unfiltered"
 			col <- pal["Discard"]
 		}
-		tlog(4,"Processing the ",filtered," graph")
+		tlog(4,"Processing the ",filt.txt," graph")
 
 		# focus on the middle of the period
 		t0 <- round(length(gs)*0.5)
@@ -261,9 +262,9 @@ for(mode in modes)
 		# perform NL regression
 		df <- data.frame(cut.deg, cut.cum)
 		fit <-nlsLM(cut.cum ~ c1*cut.deg^c2, 
-					start=list(c1=val1, c2=val2),
-					data=df,
-					control=list(maxiter=200))
+			start=list(c1=val1, c2=val2),
+			data=df,
+			control=list(maxiter=200))
 		print(summary(fit))
 
 		####################################
@@ -272,7 +273,7 @@ for(mode in modes)
 		# create/complete figure
 		if(!filts[f])
 		{	# open plot file
-			plot.file <- get.path.stats.topo(net.type="static", mode="scenes", meas.name=MEAS_DEGREE, filtered="both", suf=paste0("pref.attach_",mode))
+			plot.file <- get.path.stats.topo(net.type="static", mode="scenes", meas.name=MEAS_MULTI_NODES, filtered="both", suf=paste0("pref-attach-",mode,"_vs_degree"))
 			tlog(2,"Plot in file ",plot.file)
 			pdf(file=paste0(plot.file,PLOT_FORMAT_PDF), bg="white")
 			# plot parameters