ego.extract | R Documentation |

`ego.extract`

takes one or more input graphs (`dat`

) and returns a list containing the egocentric networks centered on vertices named in `ego`

, using adjacency rule *neighborhood* to define inclusion.

ego.extract(dat, ego = NULL, neighborhood = c("combined", "in", "out"))

`dat` |
one or more graphs. |

`ego` |
a vector of vertex IDs, or |

`neighborhood` |
the neighborhood to use. |

The egocentric network (or “ego net”) of vertex *v* in graph *G* is defined as *G[v U N(v)]* (i.e., the subgraph of *G* induced by *v* and its neighborhood). The neighborhood employed by `ego.extract`

is selected by the eponymous argument: `"in"`

selects in-neighbors, `"out"`

selects out-neighbors, and `"combined"`

selects all neighbors. In the event that one of the vertices selected by `ego`

has no qualifying neighbors, `ego.extract`

will return a degenerate (1 by 1) adjacency matrix containing that individual's diagonal entry.

Vertices within the returned matrices are maintained in their original order, save for ego (who is always listed first). The ego nets themselves are returned in the order specified in the `ego`

parameter (or their vertex order, if no value was specified).

`ego.extract`

is useful for finding local properties associated with particular vertices. To compute functions of neighbors' covariates, see `gapply`

.

A list containing the adjacency matrices for the ego nets of each vertex in `ego`

.

Carter T. Butts buttsc@uci.edu

Wasserman, S. and Faust, K. (1994). *Social Network Analysis: Methods and Applications.* Cambridge: Cambridge University Press.

`gapply`

#Generate a sample network g<-rgraph(10,tp=1.5/9) #Extract some ego nets g.in<-ego.extract(g,neighborhood="in") g.out<-ego.extract(g,neighborhood="out") g.comb<-ego.extract(g,neighborhood="in") #View some networks g.comb #Compare ego net size with degree all(sapply(g.in,NROW)==degree(g,cmode="indegree")+1) #TRUE all(sapply(g.out,NROW)==degree(g,cmode="outdegree")+1) #TRUE all(sapply(g.comb,NROW)==degree(g)/2+1) #Usually FALSE! #Calculate egocentric network density ego.size<-sapply(g.comb,NROW) if(any(ego.size>2)) sapply(g.comb[ego.size>2],function(x){gden(x[-1,-1])})

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