concerts {manet}

Concerts synthetic network

Description

Synthetic data matrix of dimension n x d, recording attendances of n=500 people to d=14 concerts from 14 different artists. There are three clusters in the data, each one corresponding to a community of fans of a specific musical genre. Overlaps of these fandoms point towards attendances dictated by artists playing music from sub-genres - such as electropop.

Usage

concerts

Format

A data frame with 500 rows and 14 variables

Details

• 14 concerts attendended: "Blondie", "Fleetwood Mac", "Paramore", "Queen", "St.Vincent", "The Queen", "Pet Shop Boys", "M83", "Daft Punk", "Goldfrapp", "Chvrches", "LaRoux", "Robyn", "BANKS"

• 500 attendees

Examples

#DATA GENERATION
z_ext <-function(x,nfac){
 nq <- length(x)
 zx <- hcube(rep(nq,nfac))
 zx <- zx[,dim(zx)[2]:1]
 z2 <- matrix(x[zx],dim(zx)[1],dim(zx)[2])
 return(z2)
 }
K=3 # main clusters: Rock (cluster h=5), Pop (cluster h=3), Electronic (cluster h=2)
K_star=2^K
n=500 #attendees
set.seed(777)
u=z_ext(0:1,K)
alpha_star=rep(0,K_star)
alpha_star=c(0.05,0.10,0.35,0.15,0.25,0.00,0.10,0.00)
index=rep(0,n)
for(i in 1:n)
 index[i]=sample(1:K_star,1,prob=alpha_star)
d=14 #concerts/artists
y<-matrix(0,n,d)
colnames(y)=c("Blondie", "Fleetwood Mac", "Paramore","Queen","St.Vincent", "The Queen",
"Pet Shop Boys","M83","Daft Punk", "Goldfrapp", "Chvrches", "LaRoux", "Robyn","BANKS")
pi.greco=matrix(0,K,d)
rownames(pi.greco)=c("Rock","Pop","Electronic")
colnames(pi.greco)=colnames(y)
pi.greco[1,]=c(0.80,0.80,0.80,0.70,0.90,0.80,0.10,0.10,0.05,0.05,0.10,0.05,0.05,0.10)
pi.greco[2,]=c(0.10,0.10,0.90,0.80,0.90,0.80,0.05,0.10,0.05,0.70,0.70,0.05,0.80,0.05)
pi.greco[3,]=c(0.05,0.05,0.05,0.10,0.05,0.05,0.80,0.90,0.90,0.80,0.70,0.80,0.90,0.90)
for (i in 1:n)
for(j in 1:d)
 y[i,j]<-rbinom(1,1,prob=ifelse(sum(u[index[i],])==0,0.00000001,min(pi.greco[,j]^u[index[i],])))
#y is the 500x14 matrix of data

#RUNNING MANET
## Not run: 
data(concerts)
start=Sys.time()
crt<-manet(concerts,K=3,maxT=5000)
finish=Sys.time()
finish-start
#Time difference of 11.58112 mins
plot(crt)
summary(crt)
alloc<-summary(crt)$actor.allocations[,2]
adjustedRandIndex(index,alloc)
#0.8420733
classError(alloc,index)$errorRate
#0.07

## End(Not run)