Case Studies: Network Analysis in R
Edmund Hart
Instructor
undirected_ment_g <- as.undirected(ment_g)
ment_edg <- cluster_edge_betweenness(undirected_ment_g)
ment_eigen <- cluster_leading_eigen(undirected_ment_g)
ment_lp <- cluster_label_prop(undirected_ment_g)
length(ment_edg)
length(ment_eigen)
length(ment_lp)
173
168
212
table(sizes(ment_edg))
2 3 4 5 6 7 8 9 11 12 18 19 20 23 24 26 28
103 21 14 7 3 3 1 2 1 2 2 1 1 1 1 2 1
31 33 38 40 41 52 58
1 1 1 1 1 1 1
table(sizes(ment_eigen))
2 3 4 5 6 7 9 10 12 18 23 26 29 30 32 34 35 58
103 22 14 7 4 3 1 1 1 1 1 1 1 1 1 1 1 1
64 66 101
1 1 1
table(sizes(ment_lp))
2 3 4 5 6 7 8 9 10 11 12 13 16 25 26 67 70
103 32 22 19 8 5 4 3 5 1 2 3 1 1 1 1 1
compare(ment_edg, ment_eigen, method = 'vi')
0.9761792
compare(ment_eigen, ment_lp, method = 'vi')
1.192238
compare(ment_lp, ment_edg, method = 'vi')
0.9631608
lrg_eigen <- as.numeric( names(ment_eigen[which(sizes(ment_eigen) > 45)]) )
eigen_sg <- induced.subgraph(ment_g, V(ment_g)[ eigen %in% lrg_eigen])
plot(eigen_sg, vertex.label = NA, edge.arrow.width = .8, edge.arrow.size = 0.2, coords = layout_with_fr(ment_sg), margin = 0, vertex.size = 6, vertex.color = as.numeric(as.factor(V(eigen_sg)$eigen)))
Mentions subgraph communities
Case Studies: Network Analysis in R