Description Usage Arguments Details Value Warning References See Also Examples
tdc
returns the temporal degree centrality for each node in a dynamic
network (sequence of graph snapshots).
1 2 |
x |
A list of adjacency matrices or a list of adjacency lists. |
type |
Data format of |
startsnapshot |
Numeric. Entry of |
endsnapshot |
Numeric. Entry of |
directed |
Logical. Set |
normalize |
Logical. Set |
centrality_evolution |
Logical. Set |
tdc
calculates the temporal degree centrality (see Kim and
Anderson, 2012), which is defined as the average degree centrality over all
snapshots.
The (normalized) temporal degree centrality values of all nodes
(TDC
). If centrality_evolution
is TRUE
an additional
matrix is returned (CentEvo
), containing the temporal centrality
value at each snapshot between startsnapshot
and endsnapshot
.
Using adjacency matrices as input exponentially increases the required memory. Use adjacency lists to save memory.
Kim, Hyoungshick and Anderson, Ross, 2012. Temporal node centrality in complex networks. Physical Review E, 85 (2).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | # Create a list of adjacency matrices, plot the corresponding graphs
# (using the igraph package) and calculating tdc
A1 <- matrix(c(0,1,0,0,0,0,
1,0,1,0,0,0,
0,1,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0), ncol=6)
A2 <- matrix(c(0,0,0,0,0,0,
0,0,1,0,0,0,
0,1,0,1,1,0,
0,0,1,0,0,0,
0,0,1,0,0,0,
0,0,0,0,0,0), ncol=6)
A3 <- matrix(c(0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0), ncol=6)
A4 <- matrix(c(0,1,0,0,0,0,
1,0,0,1,0,0,
0,0,0,0,0,0,
0,1,0,0,0,0,
0,0,0,0,0,0,
0,0,0,0,0,0), ncol=6)
library(igraph)
par(mfrow=c(2,2))
Layout <-
layout_in_circle(graph_from_adjacency_matrix(A1, mode = "undirected"))
plot(graph_from_adjacency_matrix(A1, "undirected"), layout=Layout)
plot(graph_from_adjacency_matrix(A2, "undirected"), layout=Layout)
plot(graph_from_adjacency_matrix(A3, "undirected"), layout=Layout)
plot(graph_from_adjacency_matrix(A4, "undirected"), layout=Layout)
As <- list(A1,A2,A3,A4)
tdc(As, "M", centrality_evolution=TRUE)
#' ### Create list of adjacency lists
Ls <- lapply(seq_along(As), function(i){
sapply(1:6, function(j){which(As[[i]][j,]==1)})
})
tdc(Ls, "L", centrality_evolution=TRUE)
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