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.Rhistory
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512
.Rhistory
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1,2)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1,2,3)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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#g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.breaks <- g.hist$breaks # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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alpha.LM
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
|
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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)
|
||||
@ -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)
|
||||
Loading…
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Reference in New Issue
Block a user