From b1017392dc2b079e92bea6e5f55ba5b0ba380d36 Mon Sep 17 00:00:00 2001 From: noah Date: Fri, 29 Apr 2022 16:20:24 -0500 Subject: [PATCH] Reading in R files --- .Rhistory | 512 +++++++++++++++++++++++++++++++++++++ Schrick-Noah_CG-Analysis.R | 22 ++ 2 files changed, 534 insertions(+) create mode 100644 .Rhistory diff --git a/.Rhistory b/.Rhistory new file mode 100644 index 0000000..3dc0977 --- /dev/null +++ b/.Rhistory @@ -0,0 +1,512 @@ +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.breaks[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4) +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2) +################# Linear model: Least-Squares Fit ################# +g.breaks <- g.hist$breaks[-c(1)] # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.breaks[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4) +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2) +################# Linear model: Least-Squares Fit ################# +g.breaks <- g.hist$breaks[-c(1,2)] # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.breaks[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4) +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2) +################# Linear model: Least-Squares Fit ################# +g.breaks <- g.hist$breaks[-c(1,2,3)] # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.breaks[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4) +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2) +################# Linear model: Least-Squares Fit ################# +g.breaks <- g.hist$breaks[-c(1)] # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.breaks[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4) +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2) +################# Linear model: Least-Squares Fit ################# +#g.breaks <- g.hist$breaks[-c(1)] # remove 0 +g.breaks <- g.hist$breaks # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.breaks[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4) +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2) +################# Linear model: Least-Squares Fit ################# +g.breaks <- g.hist$breaks[-c(1)] # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.probs[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4) +alpha.LM +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2) +################# Linear model: Least-Squares Fit ################# +g.breaks <- g.hist$breaks[-c(1)] # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.probs[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin)) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4) +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2) +################# Linear model: Least-Squares Fit ################# +g.breaks <- g.hist$breaks[-c(1)] # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.probs[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin)) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4) +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2) +################# Linear model: Least-Squares Fit ################# +g.breaks <- g.hist$breaks[-c(1)] # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.probs[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin)) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4) +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3) +################# Linear model: Least-Squares Fit ################# +g.breaks <- g.hist$breaks[-c(1)] # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.probs[nz.probs.mask] +#plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin)) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3) +plot(yeast) +hist(yeast) +hist(g.vec) +g.pois +g.mean +alpha.LM +alpha.ML +degree(g) +sort(degree(g)) +sort(degree(g),decreasing=FALSE) +sort(degree(g),decreasing=F) +sort(degree(g),decreasing=false) +sort(degree(g), decreasing = TRUE) +head(sort(degree(g), decreasing = TRUE)) +stddev(degree(g)) +sd(degree(g)) +tail(sort(degree(g), decreasing = TRUE)) +plot(log(g.breaks.clean), log(g.probs.clean)) +# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course +# Degree Distribution +# Professor: Dr. McKinney, Spring 2022 +# Noah Schrick - 1492657 +library(igraph) +library(igraphdata) +data(yeast) +g <- yeast +g.netname <- "Yeast" +################# Set up Work ################# +g.vec <- degree(g) +g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname, +" Network")) +legend("topright", c("Guess", "Poisson", "Least-Squares Fit", +"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6", +"#006CD1", "#E66100", "#D35FB7")) +g.mean <- mean(g.vec) +g.seq <- 0:max(g.vec) # x-axis +################# Guessing Alpha ################# +alpha.guess <- 1.5 +lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3) +################# Poisson ################# +g.pois <- dpois(g.seq, g.mean, log=F) +lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3) +################# Linear model: Least-Squares Fit ################# +g.breaks <- g.hist$breaks[-c(1)] # remove 0 +g.probs <- g.hist$density[-1] # make lengths match +# Need to clean up probabilities that are 0 +nz.probs.mask <- g.probs!=0 +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.probs[nz.probs.mask] +plot(log(g.breaks.clean), log(g.probs.clean)) +g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean)) +summary(g.fit) +alpha.LM <- coef(g.fit)[2] +lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3) +################# Max-Log-Likelihood ################# +n <- length(g.breaks.clean) +kmin <- g.breaks.clean[1] +alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin)) +alpha.ML +lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3) +plot(log(g.breaks.clean), log(g.probs.clean)) +g.breaks.clean <- g.breaks[nz.probs.mask] +g.probs.clean <- g.probs[nz.probs.mask] +plot(log(g.breaks.clean), log(g.probs.clean)) +graphvizCapabilities()$layoutTypes +library(igraph) +library(sna) +library(Rgraphviz) # Reading graphviz' "dot" files +graphvizCapabilities()$layoutTypes +car.adj <- agread("./CG_Files/Network_1/DOTFILE.dot", layoutType="dot",layout=FALSE) # Large: ~1.9G +################# Read in the previously generated networks ################# +# If sourcing: +#setwd(getSrcDirectory()[1]) +# If running: +setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) +car.adj <- agread("./CG_Files/Network_1/DOTFILE.dot", layoutType="dot",layout=FALSE) # Large: ~1.9G +plot(car.adj) diff --git a/Schrick-Noah_CG-Analysis.R b/Schrick-Noah_CG-Analysis.R index e69de29..eb2111b 100644 --- a/Schrick-Noah_CG-Analysis.R +++ b/Schrick-Noah_CG-Analysis.R @@ -0,0 +1,22 @@ +# Final Project for the University of Tulsa's CS-7863 Network Theory Course +# Compliance Graph Analysis +# Professor: Dr. McKinney, Spring 2022 +# Noah L. Schrick - 1492657 + +library(igraph) +library(sna) +library(Rgraphviz) # Reading graphviz' "dot" files + + +################# Read in the previously generated networks ################# +# If sourcing: +#setwd(getSrcDirectory()[1]) +# If running: +setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) +car.adj <- agread("./CG_Files/Network_1/DOTFILE.dot", layoutType="dot",layout=FALSE) # Large: ~1.9G +hipaa.adj <- agread("./CG_Files/Network_2/DOTFILE.dot") # Medium: ~0.9G +PCI.adj <- agread("./CG_Files/Network_3/DOTFILE.dot") # Small: ~3M + +plot(car.adj) +plot(hipaa.adj) +plot(PCI.adj)