Hello,
I have a series of graphs created from dense matrices such as
r1 <- c(NA, 0.2, 0.3, 0)
r2 <- c(NA, NA, 0.4, 0.6)
r3 <- c(NA, NA, NA, 0.1)
r4 <- rep(NA, 4)
m <- rbind(r1,r2,r3,r4)
rownames(m) <- seq(1:4)
colnames(m) <- seq(1:4)
m
# 1 2 3 4
# 1 NA 0.2 0.3 0.0
# 2 NA NA 0.4 0.6
# 3 NA NA NA 0.1
# 4 NA NA NA NA
For this matrix I create a graph with
g <- graph.adjacency(m, mode="undirected", weighted=TRUE, diag=FALSE)
My real graphs are not extremely large (the largest has 11249 nodes) but
they are pretty dense: as in the example above nodes have relations with
almost every other nodes, but without any multiedge. Yet each edge is
weighted. The "weight" attribute of the edge is the closeness of the
pair of nodes. That is, for example an edge with a "weight" of 0.5
indicates that the endpoints are closer than a endpoints with an edge of
0.3. I am interested in drawing communities that reflect the "closeness"
among nodes.
Then I want to apply a community detection algorithm which will evaluate
graph structure mainly on edges' weights.
I tested
fastgreedy.community(g)
on my small graph and it works pretty well in dividing nodes according
to edges' weight.
But I wonder if I should use other algos that better capture the
weighted structure of my graph.
Thanks
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