# Networks With R

# Networks With R

### Padgett Florentine's wedding dataset is quite fascinating — and it can be used to help you understand how to create networks with R.

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In order to practice with network data with R, we have been playing with the Padgett (1994) Florentine’s wedding dataset (discussed in the lecture). The dataset is available here:

```
> library ( network )
> data(flo)
> nflo plot(nflo, displaylabels = TRUE,
+ boxed.labels =
+ FALSE)
```

The next step was to move from the network package to igraph. Since we have the adjacency matrix, we can use it:

```
> iflo=graph_from_adjacency_matrix(flo,
+ mode = "undirected")
> plot(iflo)
```

The good thing is that a lot of functions are available. For instance, we can get shortest paths between two specific nodes. And we can give appropriate colors to the nodes that we’ll cross:

```
> AP=all_shortest_paths(iflo,
+ from="Peruzzi",
+ to="Ginori")
> L=AP$res[[1]]
> V(iflo)$color="yellow"
> V(iflo)$color[L[2:4]]="light blue"
> V(iflo)$color[L[c(1,5)]]="blue"
> plot(iflo)
```

We can also visualize edges, but I found it slightly more complicated (to extract edges from the output)

```
> liens=c(paste(as.character(L)[1:4],
+ "--",
+ as.character(L)[2:5],sep=""),
+ paste(as.character(L)[2:5],
+ "--",
+ as.character(L)[1:4],sep=""))
> df=as.data.frame(ends(iflo,E(iflo)))
> names(df)=c("src","target")
> lstn=sort(unique(c(as.character(df[,1]),as.character(df[,2]),"Pucci")))
> Eliens=paste(as.numeric(factor(df[,1],levels=lstn)),"--",
+ as.numeric(factor(df[,2],levels=lstn)),sep="")
> EU=unlist(lapply(Eliens,function(x) x%in%liens))
> E(iflo)$color=c("grey","black")[1+EU]
> plot(iflo)
```

But it works. It is also possible to use some D3js visualization:

```
> library( networkD3 )
> simpleNetwork (df)
```

Then, the next question was to add a vertice to the network. The most simple way to do it is probability through the adjacency matrix:

```
> flo2=flo
> flo2["Pucci","Bischeri"]=1
> flo2["Bischeri","Pucci"]=1
> nflo2 plot(nflo2, displaylabels = TRUE,
+ boxed.labels =
+ FALSE)
```

Then, we’ve been playing with centrality measures:

`> plot(iflo,vertex.size=betweenness(iflo))`

The goal was to see how related they were. Here, for all of them, “Medici” is the central node. But what about the others?

```
> B=betweenness(iflo)
> C=closeness(iflo)
> D=degree(iflo)
> E=eigen_centrality(iflo)$vector
> base=data.frame(betw=B,close=C,deg=D,eig=E)
> cor(base)
betw close deg eig
betw 1.0000000 0.5763487 0.8333763 0.6737162
close 0.5763487 1.0000000 0.7572778 0.7989789
deg 0.8333763 0.7572778 1.0000000 0.9404647
eig 0.6737162 0.7989789 0.9404647 1.0000000
```

Those measures are quite correlated. It is also possible to use a hierarchical graph to visualize how close those centrality measures can be:

```
> H=hclust(dist(t(base)),
+ method="ward")
> plot(H)
```

Instead of looking at values of centrality measures, it is possible to looks are ranks

```
> rbase=base
> for(i in 1:4) rbase[,i]=rank(base[,i])
> H=hclust(dist(t(rbase)),
+ method="ward")
> plot(H)
```

Here the eigenvector measure is very close to the degree of vertices.

Finally, it is possible to seek clusters (in the context of coalition here, in case a war should start between those families): `> kc <- fastgreedy.community ( iflo )`

.

Here, we have three classes (+1 for the node that is disconnected from the other families):

```
> V(iflo)$color=c("yellow","orange",
+ "light blue")[membership ( kc )]
> plot(iflo)
```

`> plot(kc,iflo)`

:

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