Benjamin Chan (https://www.facebook.com/benjamin.ks.chan)
Build off of the first assignment from the Coursera Social Network Analysis course. Below are part of the instructions from the first assignment from that course. After downloading my Facebook network data, I examine centrality and communities in the network using R version 3.1.1 (2014-07-10) with the igraph package. I used the RStudio IDE with Knitr to generate this HTML file. This analysis was run on 2015-01-02 05:51:21.
The following are instructions for downloading my own GML file is below. These instructions were taken from assignment 1 from the fall 2012 Coursera Social Network Analysis course.
In order to get your own network, complete the following steps:
- Go to http://snacourse.com/getnet
- Choose which user data (e.g. "wall posts count") you'd like to include, for this assignment no additional data is necessary, but whatever you do download, you can visualize/analyze (profile age rank: oldest profile = highest value, declarative intensity (length of text in fields like activities, books, etc.)
- Save the .gml file and load it into Gephi using "File -> Open...".
Read the GML file.
setwd("~/GitHub repositories/FacebookNetworkAnalysis")
require(igraph, quietly=TRUE)
G <- read.graph(file="ChanFacebook.gml", format="gml")My GML file was last modified on 2015-01-02 04:46:21. As of that date, there are 374 nodes.
Create first name and initials vectors from names. I'll want to use these to label nodes when plotting the network.
listName <- strsplit(V(G)$label, " ")
nameF <- sapply(listName, head, 1)
nameL <- sapply(listName, tail, 1)
initF <- substr(nameF, 1, 1)
initL <- substr(nameL, 1, 1)
initials <- paste0(initF, initL)
label <- nameF
head(label)## [1] "Christopher" "David" "Kati" "Carla" "Kevin"
## [6] "Ben"
I'm interested in two questions:
- What communities exist in my network?
- Who are the people who are "bridges" across communities?
Here's a link to Wikipedia for some background information on centrality.
Calculate degree centrality. This ends up not being too useful. Degree is really just a measure of how connected someone is. In Facebook terms, it's who has the most friends within my network.
deg <- degree(G)
summary(deg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 5 11 16 22 93
The median degree, or number of friends, was 11. The highest number of friends a person in my network has was 93.
Even though I don't want to focus on degree centrality, let's see who are the most connected people in my network.
require(xtable, quietly=TRUE)
lim <- sort(deg, decreasing=TRUE)[round(vcount(G) * 0.05)]
top <- data.frame("Name"=V(G)$label, "Degree"=deg)
top <- subset(top, deg >= lim)
top <- top[order(top$Degree, decreasing=TRUE),]
print(xtable(top, digits=0), type="html", include.rownames=FALSE)| Name | Degree |
|---|---|
| Yassine Diboun | 93 |
| Todd Janssen | 78 |
| Glenn Tachiyama | 75 |
| Jason Leman | 72 |
| Charles Replogle | 67 |
| Paul Nelson | 66 |
| Willie McBride | 65 |
| Trevor Hostetler | 63 |
| Sarah Duncan | 62 |
| Jennifer Love | 62 |
| Samantha de la Vega | 60 |
| Kevin Karr | 59 |
| Anne Crispino-Taylor | 59 |
| Renee Seker | 56 |
| Larry Stephens | 56 |
| Cheri Redwine | 55 |
| Sean Meissner | 55 |
| T.J. Ford | 55 |
| Moe Codino | 54 |
Calculate closeness centrality. Closeness is a measure of how many steps are required to access every other node. It's a measure of how close a node is to all the action. A person with high closeness, however, doesn't necessarily have to have very many friends or be in between relationship.
close <- closeness(G)
summary(close)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.17e-06 8.25e-05 8.42e-05 7.81e-05 8.50e-05 8.72e-05
Again, I don't want to focus on closeness centrality since it's not really what I'm after in this analysis, so I won't say anything more about it.
Calculate betweenness centrality. I'm going to focus on betweenness since it's going to get after one of my analysis questions, who are the "bridges" between people and communities? Betweenness is a measure of how often a node is in the pathway between two other nodes. I.e., a person with high betweenness can be a key player in introducing a large group of friends to another large group of friends. Such a person doesn't necessarily have to have a large number of friends themselves. But they could be in a unique position of influence in the network.
Plot a histogram of betweenness scores.
require(ggplot2, quietly=TRUE)
btwn <- betweenness(G)
summary(btwn)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 1 42 513 415 14900
qplot(btwn, binwidth=1000)
The absolute value of the scores don't mean much. But their relative values tell the story. There are a number of people with extremely high betweenness scores. Who are these people? List the 25 of people with the highest betweeness scores.
rank <- length(btwn) - rank(btwn) + 1
top <- data.frame("Rank"=rank, "Name"=V(G)$label, "Betweenness"=btwn)
lim <- sort(btwn, decreasing=TRUE)[round(vcount(G) * 0.05)]
# top <- subset(top, btwn >= lim)
top <- subset(top, rank <= 25)
top <- top[order(top$Betweenness, decreasing=TRUE),]
print(xtable(top, digits=0), type="html")| Rank | Name | Betweenness | |
|---|---|---|---|
| 62 | 1 | Cat Buckley | 14905 |
| 150 | 2 | Heather Fowler | 13894 |
| 299 | 3 | Evan Freeman | 10927 |
| 168 | 4 | Annie Bencomo | 8748 |
| 82 | 5 | Marc Baumgartner | 8019 |
| 86 | 6 | Lucia Longoria | 4373 |
| 61 | 7 | Yassine Diboun | 4226 |
| 191 | 8 | Allison Cox | 3897 |
| 107 | 9 | Julie Honse | 3714 |
| 200 | 10 | Christopher J. Barker | 3413 |
| 7 | 11 | Jeff Waskowiak | 3214 |
| 336 | 12 | Henry Barrera | 3096 |
| 185 | 13 | Gregory Gourdet | 3004 |
| 179 | 14 | Johnny Buell | 2964 |
| 224 | 15 | Sandy Bacharach | 2744 |
| 217 | 16 | Fernando Viciconte | 2454 |
| 194 | 17 | Nadia Khater | 2401 |
| 173 | 18 | Glenn Tachiyama | 2297 |
| 20 | 19 | Alex Asselin | 2242 |
| 316 | 20 | Erin 'Davis' Thekkedom | 2134 |
| 127 | 21 | Rebecca Ettlinger Waltz | 2129 |
| 243 | 22 | Darin Swanson | 2091 |
| 19 | 23 | James Wallace | 2048 |
| 22 | 24 | Bekah Wolf | 1995 |
| 245 | 25 | Nancy Lee | 1964 |
Plot the association between degree centrality and betweenness centrality. See if there are any highly influential people (betweenness) who also have a high number of friends (degree).
rsq <- format(cor(deg, btwn) ^2, digits=3)
cntrl <- data.frame(deg, btwn, close)
ggplot(cntrl, aes(x=deg, y=btwn)) +
geom_jitter(alpha=1/2) +
scale_y_log10() +
labs(x="Degree", y="Betweenness") +
annotate("text", label=paste("R-sq =", rsq), x=+Inf, y=1, hjust=1)
Find communities using the edge betweenness algorithm.
C <- edge.betweenness.community(G)
sizesOrdered <- sizes(C)[order(sizes(C), decreasing=TRUE)]
sizesOrdered## Community sizes
## 6 7 15 4 11 1 5 10 17 13 8 9 18 33 2 19 14 23 25 27 34 41 42 3 12
## 88 50 38 29 27 18 14 14 12 11 8 5 5 4 3 3 2 2 2 2 2 2 2 1 1
## 16 20 21 22 24 26 28 29 30 31 32 35 36 37 38 39 40 43 44 45 46 47 48 49 50
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 51 52 53 54
## 1 1 1 1
maxComm <- 11
commThreshold <- sizesOrdered[maxComm]There are 11 clearly large communities in my network. Large is defined as having more than 8 people.
Set the 12 community as a junk community.
C$membership[!(C$membership %in% as.numeric(names(sizesOrdered[1:maxComm])))] <- 999
C$membership <- unclass(factor(C$membership))
table(C$membership)##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 18 29 14 88 50 8 14 27 11 38 12 65
Scale the size of a node's plotting symbol according to its betweenness centrality score. Scaling is by percentile (75%, 90%, 95%, 97.5%, 99%).
q <- quantile(btwn, probs=c(0, 0.75, 0.9, 0.95, 0.975, 0.99, 1))
size <- cut(btwn, breaks=q, include.lowest=TRUE, dig.lab=5)
summary(size)## [0,415] (415,1279] (1279,2171.9] (2171.9,3348.1]
## 280 56 19 9
## (3348.1,8215.8] (8215.8,14905]
## 6 4
V(G)$size <- unclass(size)Label the nodes with the top betweenness centrality within each community. This will help make sense of who are the important links between each community.
labNode <- nameF
labNode[labNode == "Marc"] <- paste(labNode[labNode == "Marc"], initL[labNode == "Marc"]) # Since there's 2 Marcs, paste their last initial to the label
rankWithin <- ave(btwn, C$membership, FUN=function(x) length(x) - rank(x) + 1)
pctWithin <- 7
topWithin <- round(pctWithin / 100 * sizes(C))
message(sprintf("The top %3.1f%% within each community leads to labelling %2.0f nodes.", pctWithin, sum(topWithin)))## The top 7.0% within each community leads to labelling 28 nodes.
# Need to find a more elegant way to do this
isLabelled1 <- C$membership == 1 & rankWithin <= topWithin[ 1]
isLabelled2 <- C$membership == 2 & rankWithin <= topWithin[ 2]
isLabelled3 <- C$membership == 3 & rankWithin <= topWithin[ 3]
isLabelled4 <- C$membership == 4 & rankWithin <= topWithin[ 4]
isLabelled5 <- C$membership == 5 & rankWithin <= topWithin[ 5]
isLabelled6 <- C$membership == 6 & rankWithin <= topWithin[ 6]
isLabelled7 <- C$membership == 7 & rankWithin <= topWithin[ 7]
isLabelled8 <- C$membership == 8 & rankWithin <= topWithin[ 8]
isLabelled9 <- C$membership == 9 & rankWithin <= topWithin[ 9]
isLabelled10 <- C$membership == 10 & rankWithin <= topWithin[10]
isLabelled11 <- C$membership == 11 & rankWithin <= topWithin[11]
isLabelled12 <- C$membership == 12 & rankWithin <= topWithin[12]
isLabelled <- isLabelled1 | isLabelled2 | isLabelled3 | isLabelled4 | isLabelled5 | isLabelled6 | isLabelled7 | isLabelled8 | isLabelled9 | isLabelled10 | isLabelled11 | isLabelled12
# isLabelledOther <- grepl("Cat$|Jackie|Moi", nameF)
# isLabelled <- isLabelled | isLabelledOther
labNode[!isLabelled] <- NA
# labNode[C$membership != 5 & nameF != "Cat"] <- NACreate a function for plot.igraph.
P <- function(vertex.label.cex=1, vertex.label.color="#0000007F") {
set.seed(seed)
plot(C, G,
vertex.label=labNode,
vertex.label.color=vertex.label.color,
vertex.label.dist=1/4,
vertex.label.family="sans",
vertex.label.cex=vertex.label.cex,
vertex.frame.color=NA,
colbar=node.color,
mark.groups=NA,
mark.col=NA,
mark.border=palette,
edge.color=c("#7F7F7F3F", "#FF00003F")[crossing(C, G)+1],
edge.width=1/2
)
}Show low resolution plot for HTML file. Set the random number seed so when a high resolution version is created, it will have the same layout as this version.
id <- communities(C)[1:maxComm]
require(RColorBrewer, quietly=TRUE)
palette <- c(brewer.pal(maxComm, "Spectral"), "gray")
node.color <- c(palette[1:maxComm], rep(palette[maxComm + 1], max(C$membership) - maxComm))
seed <- file.info("ChanFacebook.gml")$mtime
P(vertex.label.cex=1)
Create high resolution PNG file for importing into presentations.
png(filename="NetworkVisualizationHiRes.png", width=12, height=12, units="in", res=600)
P(vertex.label.cex=1/2, vertex.label.color="#000000")
dev.off()## pdf
## 2
Create a data frame of the network.
D <- data.frame(name=paste(nameF, nameL), comm=C$membership, isLabelled, btwn, close, deg)
D[order(D$comm, !D$isLabelled), ]## name comm isLabelled btwn close deg
## 30 Judy Kroone 1 TRUE 1.271e+03 8.443e-05 17
## 1 Christopher Chan 1 FALSE 6.521e+00 8.214e-05 13
## 3 Kati Wu 1 FALSE 7.949e-01 8.214e-05 13
## 16 Eric Chan 1 FALSE 2.652e+00 8.216e-05 16
## 17 Derek Chan 1 FALSE 2.652e+00 8.216e-05 16
## 23 ��� ��� 1 FALSE 1.590e+00 8.214e-05 14
## 45 Jason Wu 1 FALSE 1.868e+00 8.215e-05 15
## 93 Jeannie Wu 1 FALSE 3.929e-01 8.212e-05 11
## 97 Kiyomi Ueno 1 FALSE 1.111e+00 8.214e-05 13
## 103 Jocelyn Chan 1 FALSE 9.417e+02 8.442e-05 15
## 111 Vania Ng 1 FALSE 3.020e-01 8.212e-05 11
## 116 Chris Sing 1 FALSE 3.020e-01 8.212e-05 11
## 143 Carter Ng 1 FALSE 2.652e+00 8.216e-05 16
## 151 Anna Liao 1 FALSE 1.115e+03 8.443e-05 17
## 178 Brian Chan 1 FALSE 2.000e-01 8.212e-05 10
## 271 Amy Lim 1 FALSE 6.560e+01 8.433e-05 3
## 279 Mable Cheung 1 FALSE 8.867e+02 8.441e-05 14
## 322 Karen Ng 1 FALSE 4.068e-01 8.212e-05 10
## 106 Marc Frommer 2 TRUE 1.211e+03 8.562e-05 33
## 264 Roger Chou 2 TRUE 9.850e+02 8.554e-05 24
## 5 Kevin Douglas 2 FALSE 3.862e+01 8.488e-05 25
## 25 Craig Dickson 2 FALSE 9.431e+01 8.495e-05 31
## 46 Michael Samarelli 2 FALSE 3.468e+02 8.400e-05 11
## 112 Kathy Klass 2 FALSE 6.370e-01 8.365e-05 12
## 133 Juli Danielle 2 FALSE 2.210e+01 8.479e-05 16
## 146 Steve Walters 2 FALSE 4.846e+02 8.511e-05 46
## 165 Christopher Warren 2 FALSE 8.893e+02 8.544e-05 28
## 166 Steven Yee 2 FALSE 9.309e+01 8.473e-05 28
## 183 May Cheng 2 FALSE 5.176e-01 8.361e-05 10
## 184 Matt Hagen 2 FALSE 1.982e+02 8.503e-05 38
## 190 Donald Mukai 2 FALSE 2.607e+00 8.454e-05 11
## 210 Andy Fritz 2 FALSE 1.374e+01 8.461e-05 19
## 215 Bob Martin 2 FALSE 1.027e+01 8.376e-05 20
## 216 Roger Michel 2 FALSE 2.879e+01 8.475e-05 22
## 219 Dave Mari 2 FALSE 2.498e+01 8.460e-05 17
## 227 Patti Krebsbach 2 FALSE 4.814e+01 8.468e-05 25
## 231 Eric Barnes 2 FALSE 1.626e+02 8.496e-05 37
## 239 David Spooner 2 FALSE 2.343e+00 8.394e-05 14
## 246 Terry Sentinella 2 FALSE 3.831e+02 8.503e-05 44
## 258 Ginger Gruber 2 FALSE 1.071e+01 8.400e-05 21
## 275 Alvin Crain 2 FALSE 1.109e+02 8.493e-05 32
## 280 Van Phan 2 FALSE 4.584e+01 8.488e-05 28
## 296 Shannon Willford 2 FALSE 1.053e+01 8.406e-05 15
## 298 Russ Smith 2 FALSE 2.251e+00 8.394e-05 14
## 311 Bill Barmore 2 FALSE 4.954e+00 8.403e-05 16
## 358 Lisa Wood 2 FALSE 2.419e+01 8.470e-05 19
## 371 Stevie Lopez 2 FALSE 8.062e+01 8.491e-05 26
## 62 Cat Buckley 3 TRUE 1.491e+04 8.674e-05 35
## 6 Ben Aldred 3 FALSE 0.000e+00 8.423e-05 2
## 20 Alex Asselin 3 FALSE 2.242e+03 8.595e-05 4
## 38 Kelly McCarthy 3 FALSE 0.000e+00 8.424e-05 3
## 87 Mark Hernandez 3 FALSE 3.257e+02 8.492e-05 3
## 88 Aaron Buckley 3 FALSE 5.914e+01 8.485e-05 8
## 148 Heather Sutch 3 FALSE 0.000e+00 8.422e-05 1
## 169 Elizabeth Buckley 3 FALSE 4.258e+01 8.485e-05 7
## 221 Conn Buckley 3 FALSE 4.916e+01 8.485e-05 7
## 232 Jackie Kersten 3 FALSE 1.600e+03 8.578e-05 5
## 273 Lea Davis 3 FALSE 5.000e-01 8.426e-05 6
## 335 Kelly Anne 3 FALSE 0.000e+00 8.422e-05 1
## 365 Setsuko Buckley 3 FALSE 0.000e+00 8.425e-05 5
## 366 Yumin Buckley 3 FALSE 0.000e+00 8.425e-05 5
## 7 Jeff Waskowiak 4 TRUE 3.214e+03 8.561e-05 30
## 61 Yassine Diboun 4 TRUE 4.226e+03 8.686e-05 93
## 82 Marc Baumgartner 4 TRUE 8.019e+03 8.619e-05 32
## 173 Glenn Tachiyama 4 TRUE 2.297e+03 8.592e-05 75
## 185 Gregory Gourdet 4 TRUE 3.004e+03 8.620e-05 37
## 243 Darin Swanson 4 TRUE 2.091e+03 8.529e-05 22
## 9 Jason Leman 4 FALSE 9.600e+02 8.604e-05 72
## 14 David Herron 4 FALSE 9.910e+00 8.526e-05 14
## 21 Ben Blessing 4 FALSE 4.908e+01 8.496e-05 23
## 32 Dana Katz 4 FALSE 2.138e+01 8.483e-05 40
## 37 Willie McBride 4 FALSE 1.170e+03 8.642e-05 65
## 52 Gary Robbins 4 FALSE 4.635e+02 8.511e-05 25
## 54 Desiree Marek 4 FALSE 3.189e+02 8.581e-05 51
## 57 Alessandra Novak 4 FALSE 6.700e+02 8.499e-05 21
## 64 Jocelyn Nelson 4 FALSE 1.061e+02 8.566e-05 31
## 67 Rhiannon Wood 4 FALSE 3.936e+00 8.470e-05 24
## 68 Jeremy Hurl 4 FALSE 4.440e+00 8.481e-05 25
## 72 Marta Fisher 4 FALSE 1.378e+01 8.484e-05 34
## 78 Neil Baker 4 FALSE 2.911e+01 8.511e-05 22
## 100 Mac Smith 4 FALSE 1.100e+01 8.473e-05 15
## 105 Christine An 4 FALSE 9.291e+01 8.508e-05 13
## 115 Sarah Bradham 4 FALSE 2.151e+02 8.592e-05 43
## 118 Jason Fedchak 4 FALSE 0.000e+00 8.448e-05 8
## 121 Sarah Shea 4 FALSE 1.397e+01 8.418e-05 7
## 123 Jennifer Allen 4 FALSE 9.087e+01 8.551e-05 27
## 124 Mary Ramsay 4 FALSE 1.896e+01 8.496e-05 25
## 126 Cheri Redwine 4 FALSE 4.177e+02 8.524e-05 55
## 129 Sean Meissner 4 FALSE 6.596e+02 8.545e-05 55
## 130 Kevin Karr 4 FALSE 6.684e+02 8.612e-05 59
## 134 Sara Lee 4 FALSE 2.623e+01 8.501e-05 29
## 145 Andrea Jarzombek-Holt 4 FALSE 2.517e+02 8.606e-05 52
## 154 T.J. Ford 4 FALSE 6.740e+02 8.593e-05 55
## 156 Olga King 4 FALSE 8.093e-01 8.435e-05 8
## 158 Leslie Gerein 4 FALSE 9.801e-01 8.387e-05 9
## 161 Heather McGrath 4 FALSE 4.180e+02 8.587e-05 37
## 172 Charles Replogle 4 FALSE 1.165e+03 8.624e-05 67
## 174 Diana Bartolus 4 FALSE 1.856e+02 8.530e-05 27
## 176 Renee Seker 4 FALSE 5.488e+02 8.635e-05 56
## 181 Joshua Marks 4 FALSE 2.481e+01 8.480e-05 10
## 186 Joel Dippold 4 FALSE 1.370e+03 8.583e-05 13
## 187 Leif Rustvold 4 FALSE 1.422e+00 8.472e-05 9
## 196 Sarah Duncan 4 FALSE 8.437e+02 8.610e-05 62
## 206 Paul Nelson 4 FALSE 6.199e+02 8.618e-05 66
## 208 Katie Christianson 4 FALSE 1.665e+02 8.563e-05 53
## 213 Hugh Davis 4 FALSE 1.191e+02 8.554e-05 41
## 214 Gregg Webber 4 FALSE 5.044e+01 8.501e-05 13
## 226 Robert Orcutt 4 FALSE 4.006e+01 8.473e-05 15
## 228 Nancy Innis 4 FALSE 0.000e+00 8.422e-05 5
## 229 Mike Davis 4 FALSE 3.197e+02 8.595e-05 51
## 230 Julie Thomas 4 FALSE 4.393e+02 8.565e-05 49
## 234 Moises Lucero 4 FALSE 1.664e+03 8.581e-05 21
## 237 Josh Owen 4 FALSE 5.224e+01 8.490e-05 22
## 238 Jesse Cox 4 FALSE 6.946e+00 8.496e-05 21
## 241 Matt Helms 4 FALSE 9.668e+01 8.565e-05 44
## 252 Angela Drake 4 FALSE 1.318e-01 8.447e-05 9
## 253 Nathan Herzog 4 FALSE 6.164e+01 8.478e-05 15
## 261 Jeannie Horton 4 FALSE 1.336e+03 8.593e-05 46
## 265 Ronda Sundermeier 4 FALSE 4.031e+02 8.575e-05 40
## 268 Jeff Boggess 4 FALSE 2.662e+02 8.598e-05 51
## 272 Samantha Vega 4 FALSE 6.353e+02 8.620e-05 60
## 276 Pam Smith 4 FALSE 1.941e+02 8.549e-05 37
## 291 Jim Kennedy 4 FALSE 1.188e+01 8.495e-05 35
## 292 Syd Long 4 FALSE 9.132e+01 8.549e-05 31
## 293 Todd Janssen 4 FALSE 1.887e+03 8.666e-05 78
## 297 Brian Janecek 4 FALSE 1.606e+02 8.570e-05 43
## 305 John Liebeskind 4 FALSE 1.603e+00 8.460e-05 9
## 314 Anne Crispino-Taylor 4 FALSE 6.855e+02 8.598e-05 59
## 315 Pete Savage 4 FALSE 2.398e+01 8.483e-05 9
## 318 Trevor Hostetler 4 FALSE 4.354e+02 8.585e-05 63
## 323 Brandon Drake 4 FALSE 3.565e+02 8.601e-05 46
## 324 Jeff McAlpine 4 FALSE 2.573e+02 8.526e-05 32
## 326 Silvia Reyes 4 FALSE 2.983e+01 8.531e-05 29
## 329 Justin Huggins 4 FALSE 1.136e+01 8.492e-05 18
## 331 Eric Lubell 4 FALSE 1.577e+02 8.558e-05 39
## 334 Rikilynn Mclenithan 4 FALSE 4.320e+01 8.522e-05 29
## 337 Rick Kneedler 4 FALSE 9.523e+01 8.541e-05 34
## 342 Mike Burke 4 FALSE 4.471e+01 8.488e-05 23
## 345 David Chilson 4 FALSE 0.000e+00 8.426e-05 6
## 346 Ricky Bartolus 4 FALSE 1.947e-01 8.461e-05 12
## 348 Larry Stephens 4 FALSE 4.323e+02 8.610e-05 56
## 356 Megan Bruce 4 FALSE 4.202e+01 8.512e-05 38
## 360 Scott Dumdi 4 FALSE 2.429e+00 8.477e-05 21
## 361 Moe Codino 4 FALSE 1.448e+02 8.553e-05 54
## 362 Juliano Wilson 4 FALSE 2.101e-01 8.462e-05 8
## 368 Dennis Gamroth 4 FALSE 1.033e+01 8.456e-05 14
## 369 Jennifer Love 4 FALSE 4.757e+02 8.615e-05 62
## 370 Keith Shishido 4 FALSE 1.604e+01 8.487e-05 18
## 373 Avery McCombs 4 FALSE 3.145e+01 8.471e-05 16
## 8 Colleen Schoonover 5 TRUE 1.067e+03 8.338e-05 19
## 299 Evan Freeman 5 TRUE 1.093e+04 8.488e-05 21
## 316 Erin Thekkedom 5 TRUE 2.134e+03 8.304e-05 8
## 332 Marcus Fischer 5 TRUE 1.771e+03 8.337e-05 19
## 31 Justin Kim 5 FALSE 2.439e+01 8.157e-05 13
## 44 Ashlyn Jaswal 5 FALSE 1.588e+00 8.078e-05 4
## 51 Ankit Upadhyayula 5 FALSE 6.213e+02 8.331e-05 29
## 59 Kim Huynh 5 FALSE 5.995e+02 8.335e-05 28
## 73 Victor So 5 FALSE 3.120e+01 8.132e-05 24
## 74 Dakota McMillan 5 FALSE 2.082e+02 8.157e-05 27
## 77 Jon Anderson 5 FALSE 3.643e+02 8.324e-05 23
## 79 Donovan Tran 5 FALSE 4.709e+02 8.304e-05 24
## 83 Kellyn Christison 5 FALSE 5.377e+02 8.207e-05 10
## 94 Megan Baxter 5 FALSE 6.110e+02 8.333e-05 14
## 153 Grace Xia 5 FALSE 2.333e+02 8.299e-05 22
## 155 Christina Hsu 5 FALSE 5.084e+02 8.326e-05 24
## 175 Bre Ongley 5 FALSE 6.259e+01 8.145e-05 15
## 177 Kyle Yasumiishi 5 FALSE 3.703e+01 8.163e-05 21
## 189 Cathy Dinh 5 FALSE 3.155e+02 8.122e-05 11
## 193 Danny Cohen 5 FALSE 1.051e+03 8.337e-05 31
## 198 Ydali Olivera 5 FALSE 1.121e+01 8.151e-05 9
## 204 Sean Morrison 5 FALSE 2.852e+01 8.127e-05 3
## 211 Nicki Parker 5 FALSE 4.702e+02 8.290e-05 12
## 254 Jordan Brazda 5 FALSE 2.130e+02 8.315e-05 10
## 256 Eric Kim 5 FALSE 1.509e+02 8.185e-05 24
## 263 Paige Singhose 5 FALSE 3.871e+02 8.321e-05 17
## 289 Briana Chui 5 FALSE 1.019e+02 8.109e-05 4
## 294 Nick Cropley 5 FALSE 6.510e+02 8.308e-05 26
## 295 Megan Schermerhorn 5 FALSE 2.515e+02 8.302e-05 22
## 300 Nick Hartley 5 FALSE 1.468e+00 8.111e-05 5
## 303 Allison Rogers 5 FALSE 6.650e+01 8.177e-05 17
## 306 DjBlast Sanchez 5 FALSE 2.456e+02 8.297e-05 12
## 308 Mindy Kirschbaum 5 FALSE 6.148e+02 8.147e-05 7
## 309 Jordan LeBle 5 FALSE 4.082e+01 8.289e-05 11
## 310 Will Kent 5 FALSE 1.563e+01 8.129e-05 19
## 313 Brian Grimsted 5 FALSE 5.882e-02 8.127e-05 4
## 317 Garrett Mann 5 FALSE 5.091e+01 8.153e-05 11
## 321 Brady Ogden 5 FALSE 1.743e+02 8.294e-05 11
## 327 Carly DeLapp 5 FALSE 3.594e+02 8.315e-05 17
## 330 Kyle Shorter 5 FALSE 5.147e+00 8.149e-05 7
## 338 Marcus Kwon 5 FALSE 4.657e+00 8.118e-05 8
## 339 Eryn Sych 5 FALSE 4.290e+01 8.163e-05 20
## 341 Vlad Shapoval 5 FALSE 3.628e+02 8.304e-05 24
## 343 Kaitie Doupé 5 FALSE 2.831e+02 8.317e-05 15
## 347 McKenna Spieth 5 FALSE 1.324e+02 8.155e-05 25
## 349 Lauren Marshall 5 FALSE 4.156e+01 8.120e-05 9
## 351 Sierra Monaco 5 FALSE 8.768e+01 8.266e-05 5
## 352 Connor Brennan 5 FALSE 1.093e+00 8.117e-05 8
## 353 Shane Brennan 5 FALSE 8.831e+00 8.131e-05 21
## 372 Kyle Shorter 5 FALSE 4.690e+00 8.114e-05 7
## 194 Nadia Khater 6 TRUE 2.401e+03 8.465e-05 9
## 10 Krystle Flerchinger 6 FALSE 1.736e+01 8.177e-05 6
## 11 Justin Pierce 6 FALSE 3.529e+01 8.296e-05 7
## 15 Amanda Smith 6 FALSE 1.198e+02 8.303e-05 9
## 24 Melissa Holmes 6 FALSE 3.285e+01 8.289e-05 8
## 95 Ryan Alice 6 FALSE 4.558e+01 8.291e-05 9
## 225 Thomas Gehrke 6 FALSE 3.463e+01 8.281e-05 8
## 240 Kellie Kutkey 6 FALSE 5.903e+01 8.277e-05 6
## 19 James Wallace 7 TRUE 2.048e+03 8.155e-05 7
## 13 Theresa Nguyen 7 FALSE 2.977e+02 8.041e-05 2
## 28 Julie Bergstrom 7 FALSE 3.333e-01 7.937e-05 4
## 66 Annette Vu 7 FALSE 6.533e+00 7.834e-05 2
## 80 Natalie Jacuzzi 7 FALSE 0.000e+00 8.040e-05 2
## 128 Carter Wallace 7 FALSE 0.000e+00 7.936e-05 3
## 139 Diane Doctor 7 FALSE 0.000e+00 8.040e-05 2
## 149 Todd Bates 7 FALSE 0.000e+00 7.729e-05 2
## 202 Katy Smith 7 FALSE 2.231e+02 7.939e-05 7
## 245 Nancy Lee 7 FALSE 1.964e+03 8.263e-05 6
## 269 Patty Lewis 7 FALSE 7.567e+00 7.939e-05 4
## 282 Marie Bates 7 FALSE 1.702e+02 7.937e-05 5
## 287 Kim Peterson 7 FALSE 0.000e+00 8.040e-05 2
## 301 Miranda Pappas 7 FALSE 7.482e+01 8.047e-05 6
## 22 Bekah Wolf 8 TRUE 1.995e+03 8.572e-05 22
## 336 Henry Barrera 8 TRUE 3.096e+03 8.527e-05 9
## 18 Donnie Drobny 8 FALSE 1.525e+02 8.480e-05 15
## 26 Nikki O'Brien 8 FALSE 5.380e-01 8.347e-05 11
## 41 Heather Wiese 8 FALSE 0.000e+00 8.457e-05 3
## 50 Carrie Yerton 8 FALSE 1.283e+03 8.442e-05 18
## 58 Chris Frazier 8 FALSE 4.471e-01 8.345e-05 8
## 69 Nicholas Burnett 8 FALSE 0.000e+00 8.293e-05 4
## 75 Jeffrey Prescott 8 FALSE 1.757e+01 8.390e-05 13
## 104 Nathan Enns 8 FALSE 6.506e+02 8.488e-05 21
## 109 Greg Pugh 8 FALSE 2.110e+02 8.477e-05 17
## 122 Kimberly Livesay 8 FALSE 6.121e+02 8.470e-05 19
## 140 Samantha Johnson 8 FALSE 1.298e+02 8.402e-05 10
## 144 Amy McBride 8 FALSE 1.477e+01 8.396e-05 14
## 163 Mike Klausman 8 FALSE 6.512e+02 8.489e-05 23
## 197 Micah Wiese 8 FALSE 1.558e+03 8.507e-05 21
## 205 Kieffer Tarbell 8 FALSE 3.440e+02 8.488e-05 22
## 222 Julie Hurliman 8 FALSE 3.103e+01 8.393e-05 17
## 251 Tonia Gebhart 8 FALSE 2.261e+01 8.393e-05 17
## 255 Debbie Tavares 8 FALSE 9.021e+01 8.400e-05 20
## 257 Heidi Baney 8 FALSE 7.847e+02 8.422e-05 11
## 278 Nathan Conant 8 FALSE 1.672e+02 8.483e-05 15
## 290 Micah Stickler 8 FALSE 7.692e-02 8.336e-05 9
## 319 Bryan Agee 8 FALSE 0.000e+00 8.247e-05 1
## 350 Heidi Baney 8 FALSE 6.534e+01 8.425e-05 7
## 355 Jennifer Waters 8 FALSE 3.895e+01 8.392e-05 15
## 374 Heidi Baney 8 FALSE 1.873e+02 8.369e-05 7
## 191 Allison Cox 9 TRUE 3.897e+03 8.511e-05 10
## 29 Carolina Main 9 FALSE 1.701e+03 8.278e-05 5
## 33 Linda Jones 9 FALSE 0.000e+00 8.128e-05 1
## 65 Elissa Kevrekian 9 FALSE 1.938e+03 8.457e-05 4
## 76 Reniera Eddy 9 FALSE 4.068e+02 8.362e-05 5
## 96 Dan Lankow 9 FALSE 5.000e-01 8.271e-05 3
## 159 Eric Main 9 FALSE 0.000e+00 8.274e-05 3
## 182 Skye Macalester 9 FALSE 0.000e+00 8.273e-05 2
## 192 Michael Cox 9 FALSE 5.102e+02 8.366e-05 6
## 207 James Kalashnikov 9 FALSE 0.000e+00 8.270e-05 2
## 333 Cory Eddy 9 FALSE 0.000e+00 8.271e-05 2
## 107 Julie Honse 10 TRUE 3.714e+03 8.648e-05 13
## 150 Heather Fowler 10 TRUE 1.389e+04 8.657e-05 5
## 168 Annie Bencomo 10 TRUE 8.748e+03 8.715e-05 20
## 35 Giovanni Bencomo 10 FALSE 8.581e+02 8.594e-05 11
## 36 Rick Rezinas 10 FALSE 5.461e+02 8.571e-05 10
## 47 Gary Blessington 10 FALSE 1.534e+02 8.581e-05 7
## 53 Stephanie Bolson 10 FALSE 2.727e+01 8.563e-05 8
## 55 Keely Phillips 10 FALSE 0.000e+00 8.499e-05 4
## 60 Eric Virshbo 10 FALSE 1.076e+00 8.353e-05 5
## 84 Melissa Hovis 10 FALSE 2.927e+01 8.443e-05 10
## 85 Amy Harris 10 FALSE 8.163e+02 8.584e-05 25
## 89 Kate Horspool 10 FALSE 3.550e+02 8.599e-05 6
## 91 Dustin Harris 10 FALSE 6.232e+02 8.580e-05 21
## 92 Alicia Fuentes 10 FALSE 3.324e+02 8.456e-05 15
## 99 Susan Kucera 10 FALSE 4.954e+00 8.434e-05 7
## 102 Dave Horspool 10 FALSE 0.000e+00 8.488e-05 4
## 108 Thomas O'Leary 10 FALSE 2.917e-01 8.384e-05 6
## 120 Debbie Ginzburg 10 FALSE 9.723e+00 8.406e-05 6
## 127 Rebecca Waltz 10 FALSE 2.129e+03 8.634e-05 26
## 131 Rachel Prewitt 10 FALSE 7.648e+02 8.568e-05 11
## 137 Nedra Rezinas 10 FALSE 1.249e+03 8.579e-05 14
## 138 Brent Harris 10 FALSE 3.704e-01 8.417e-05 6
## 164 Amara Zee 10 FALSE 2.419e+01 8.467e-05 10
## 167 Michael Mouton 10 FALSE 4.788e+00 8.430e-05 8
## 179 Johnny Buell 10 FALSE 2.964e+03 8.587e-05 21
## 199 Holly Fraser-Witt 10 FALSE 7.138e+01 8.560e-05 5
## 200 Christopher Barker 10 FALSE 3.413e+03 8.616e-05 32
## 203 Jason Post 10 FALSE 1.660e+00 8.460e-05 5
## 242 Tracy Meese 10 FALSE 0.000e+00 8.488e-05 4
## 259 Mike Smith 10 FALSE 0.000e+00 8.375e-05 2
## 260 Matt Love 10 FALSE 9.098e+02 8.481e-05 20
## 262 Laura Davis 10 FALSE 9.012e-01 8.426e-05 7
## 266 Sher Sinda 10 FALSE 2.000e-01 8.422e-05 6
## 274 John Stevens 10 FALSE 2.714e-01 8.431e-05 5
## 320 Sean Politz 10 FALSE 1.580e+02 8.566e-05 11
## 325 David Dvorak 10 FALSE 8.480e+02 8.545e-05 15
## 344 Sarah Dyste 10 FALSE 1.250e-01 8.431e-05 5
## 363 Chanda Gandhi 10 FALSE 4.303e+00 8.430e-05 7
## 86 Lucia Longoria 11 TRUE 4.373e+03 8.490e-05 16
## 42 Richmond Fontaine 11 FALSE 0.000e+00 8.258e-05 6
## 90 Angie Burr 11 FALSE 0.000e+00 8.250e-05 2
## 110 Cheryl Ann 11 FALSE 1.167e+01 8.313e-05 5
## 125 Matt Moore 11 FALSE 7.592e+01 8.322e-05 12
## 141 Marne Manoukian 11 FALSE 6.500e+02 8.544e-05 5
## 170 Kristen Broyer 11 FALSE 8.580e+01 8.371e-05 9
## 217 Fernando Viciconte 11 FALSE 2.454e+03 8.374e-05 10
## 247 Pixelface Creative 11 FALSE 2.583e+00 8.257e-05 4
## 277 Jolene Kawecki 11 FALSE 9.778e+00 8.317e-05 7
## 284 Megan Brown 11 FALSE 1.598e+01 8.318e-05 8
## 286 Stacy Benjamin 11 FALSE 1.667e-01 8.259e-05 7
## 162 Dawn Jones-Redstone 12 TRUE 1.396e+03 8.455e-05 2
## 201 Mandy Wilson 12 TRUE 1.090e+03 8.637e-05 5
## 224 Sandy Bacharach 12 TRUE 2.744e+03 8.615e-05 7
## 250 Ann Wallace 12 TRUE 1.723e+03 8.581e-05 8
## 281 Frances Favela 12 TRUE 1.364e+03 8.054e-05 3
## 2 David Allamon 12 FALSE 0.000e+00 8.493e-05 3
## 4 Carla Owen 12 FALSE 0.000e+00 8.336e-05 5
## 12 Naomi Fishman 12 FALSE 6.860e+02 7.840e-05 3
## 27 Erica Allison 12 FALSE 0.000e+00 7.168e-06 0
## 34 Tina Slee 12 FALSE 0.000e+00 8.114e-05 1
## 39 Makiko Yamashita 12 FALSE 3.440e+02 8.347e-05 2
## 40 Amy Begley 12 FALSE 5.135e+00 8.472e-05 7
## 43 Nic Lamb 12 FALSE 0.000e+00 8.368e-05 1
## 48 Orna Izakson 12 FALSE 0.000e+00 8.259e-05 3
## 49 Elizabeth Cole 12 FALSE 0.000e+00 8.368e-05 1
## 56 Shawn Mullaly 12 FALSE 0.000e+00 8.269e-05 1
## 63 Caroline Kobin 12 FALSE 4.250e+02 8.598e-05 4
## 70 Vincent Granville 12 FALSE 0.000e+00 7.188e-06 1
## 71 Jack Cheng 12 FALSE 0.000e+00 8.164e-05 1
## 81 Mark Novak 12 FALSE 8.184e+01 8.386e-05 5
## 98 Ann Visan 12 FALSE 0.000e+00 7.188e-06 1
## 101 Susan Kelley 12 FALSE 8.287e+02 8.570e-05 7
## 113 Keith Parks 12 FALSE 0.000e+00 8.337e-05 4
## 114 Nicole Busto 12 FALSE 3.440e+02 7.635e-05 2
## 117 Julie Fukuda 12 FALSE 0.000e+00 7.188e-06 1
## 119 Ramona DeNies 12 FALSE 0.000e+00 7.168e-06 0
## 132 Kristen Backeberg 12 FALSE 1.790e+02 8.576e-05 6
## 135 Aviva Brown 12 FALSE 0.000e+00 8.259e-05 3
## 136 Urko Larrakoetxea 12 FALSE 0.000e+00 7.188e-06 1
## 142 Karinna Jones-Ianello 12 FALSE 1.664e+01 8.479e-05 4
## 147 Jenny Nicole 12 FALSE 0.000e+00 8.358e-05 2
## 152 Melissa Powell 12 FALSE 0.000e+00 7.839e-05 2
## 157 Nicole Curcio 12 FALSE 0.000e+00 7.168e-06 0
## 160 Ali Jessie 12 FALSE 1.214e+02 8.455e-05 4
## 171 Esther Lai 12 FALSE 0.000e+00 7.226e-06 2
## 180 April Brewer 12 FALSE 0.000e+00 7.188e-06 1
## 188 Elizabeth Rollerson 12 FALSE 3.131e+02 8.531e-05 7
## 195 Lance Adkins 12 FALSE 0.000e+00 7.168e-06 0
## 209 Lisa Kith 12 FALSE 0.000e+00 7.226e-06 2
## 212 Lucien Kress 12 FALSE 0.000e+00 7.188e-06 1
## 218 Kyle Pak 12 FALSE 2.000e+00 7.226e-06 3
## 220 Sean Kilpatrick 12 FALSE 0.000e+00 7.168e-06 0
## 223 David Yan 12 FALSE 0.000e+00 7.226e-06 1
## 233 Kush Patel 12 FALSE 0.000e+00 7.188e-06 1
## 235 Rob Coomer 12 FALSE 0.000e+00 7.188e-06 1
## 236 David Dranchak 12 FALSE 0.000e+00 7.168e-06 0
## 244 Joe Ferguson 12 FALSE 0.000e+00 8.441e-05 5
## 248 Leah Williams 12 FALSE 0.000e+00 7.440e-05 1
## 249 Todd Evans 12 FALSE 1.439e-01 8.423e-05 3
## 267 Jenna Jones 12 FALSE 3.455e+02 7.937e-05 4
## 270 Anthony Petrarca 12 FALSE 0.000e+00 7.188e-06 1
## 283 Patrick Romano 12 FALSE 0.000e+00 7.168e-06 0
## 285 Justine Vanderpool 12 FALSE 0.000e+00 7.726e-05 1
## 288 Eric Barten 12 FALSE 2.936e-01 8.455e-05 4
## 302 Ryan Parrett 12 FALSE 0.000e+00 7.168e-06 0
## 304 Katie Lessner 12 FALSE 0.000e+00 7.188e-06 1
## 307 Mario Delgado 12 FALSE 0.000e+00 7.168e-06 0
## 312 Lindsey Barber 12 FALSE 0.000e+00 8.078e-05 1
## 328 Chris Fanning 12 FALSE 0.000e+00 7.168e-06 0
## 340 Lauren Weigel 12 FALSE 0.000e+00 7.925e-05 1
## 354 Lance Carion 12 FALSE 0.000e+00 7.168e-06 0
## 357 Jennifer Ha 12 FALSE 0.000e+00 7.168e-06 0
## 359 Irina St 12 FALSE 0.000e+00 7.168e-06 0
## 364 Oscar Sanchez 12 FALSE 0.000e+00 7.168e-06 0
## 367 Rebekah Chou 12 FALSE 2.851e+01 8.485e-05 5
- Community 1 is family and relatives.
- Community 2 is Marathon Maniacs.
- Community 3 is trail runners, including Trail Factor, Animal Athletics, and BananaSluggers.
- Community 4 is Union High School, mostly students with non-math teachers and staff. Some Mountain View students who are connected to Union people.
- Community 5 is the Union High School math teachers.
- Community 6 is OHSU BICC.
- Community 7 is my Concordia MATE cohort.
- Community 8 is Cat's side of the family and her friends.
- Community 9 is a secondary group of Portland friends.
- Community 10 is a core group of Portland friends.
- Community 11 is a secondary group of Portland friends that has connections to Community 9.
- Community 12 is the misfits community of people that either
- Don't fit in with a larger community within my network, or
- Truly bridge the other 11 well-defined communities to an extent that they don't belong in a single community
- There are a number of key people that connect 2 or more communities.
Create interactive 3-D high resolution.
Open the network.html file in a browser.
require(networkD3)## Loading required package: networkD3
## Warning: package 'networkD3' was built under R version 3.1.2
edges <- data.frame(get.edgelist(G))
names(edges) <- c("source", "target")
edges$source <- edges$source - 1
edges$target <- edges$target - 1
edges$value <- 1
vertices <- D[, c("name", "comm")]
names(vertices) <- c("name", "group")
vertices$name <- as.character(vertices$name)
N <- forceNetwork(Links=edges, Nodes=vertices,
Source="source", Target="target", Value = "value",
NodeID = "name", Group="group",
opacity=3/4)
saveNetwork(N, "network.html")Don't use rglplot.
l <- layout.fruchterman.reingold(G, dim=3)
rglplot(G,
layout=l,
vertex.label=labNode,
vertex.label.color="black",
vertex.label.dist=1/4,
vertex.label.family="sans",
vertex.label.cex=1/2,
vertex.frame.color=NA,
edge.color=c("gray", "red")[crossing(C, G)+1],
edge.width=1/8
)
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