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03-community.Rmd
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# 社团结构划分
*生成GN Benchmark网络；用恒猴网络来尝试对有向、加权、无向无权网络模型下进行社团结构划分。*
```{r gn-benchmark}
# failure attempts of GN Benchmark
mean_k <- 16
n_vertices <- 128
kin <- 12
kout <- mean_k - kin
graph_config <- tibble(
vertex_id = 1:n_vertices,
com_id = rep(1:4, each = n_vertices / 4),
deg_all = rbinom(n_vertices, 128, 16 / 128),
deg_in = round(deg_all * kin / mean_k),
deg_out = deg_all - deg_in
)
graph_edges <- expand_grid(
tibble(V1 = 1:n_vertices, C1 = rep(1:4, each = n_vertices / 4)),
tibble(V2 = 1:n_vertices, C2 = rep(1:4, each = n_vertices / 4))
) %>%
filter(V1 < V2)
deg_vertices <- rbinom(n_vertices, 128, 16 / 128)
deg_in <- round(deg_vertices * kin / mean_k)
deg_out <- deg_vertices - deg_in
p_connected <- mean_k / n_vertices
set.seed(20191210)
graph_config <- tibble(
kin = 10:15,
kout = mean_k - kin,
pin = kin / kout * p_connected,
pout = kout / kin * p_connected
) %>%
mutate(
graph = map(
pin, pout,
~ expand_grid(
tibble(V1 = 1:n_vertices, C1 = rep(1:4, each = n_vertices / 4)),
tibble(V2 = 1:n_vertices, C2 = rep(1:4, each = n_vertices / 4))
) %>%
filter(V1 < V2) %>%
mutate(
rnd = runif(nrow(.)),
is_connected = if_else(
C1 == C2,
rnd < .x,
rnd < .y
)
) %>%
filter(is_connected) %>%
select(V1, V2) %>%
as.matrix() %>%
graph_from_edgelist(directed = FALSE)
)
)
```
图\@ref(fig:cluster-comparison)画出了四种条件（有向加权、有向无权、无向加权、无向无权）下的社团结构划分结果。可见权重对于结果的影响大于是否有向的影响。
```{r cluster-comparison, fig.width=6, fig.height=4, fig.cap='社团结构划分对比'}
# directed and weighted
data_rhesus <- read_tsv(
"datasets/恒猴数据.txt",
col_names = c("V1", "V2", "weight")
)
par(mar = c(0, 0, 2, 0) + 0.1, mfrow = c(2, 2))
graph_rhesus_dir_wei <- graph_from_data_frame(data_rhesus)
com_dir_wei <- cluster_walktrap(graph_rhesus_dir_wei)
plot(
com_dir_wei,
graph_rhesus_dir_wei,
vertex.size = 8,
vertex.label.cex = 0.6,
vertex.label.color = "white",
edge.curved = 0,
edge.arrow.size = 0.5,
edge.arrow.width = 0.5,
edge.label = E(graph_rhesus_dir_wei)$weight,
edge.label.cex = 0.6,
edge.label.color = "black",
# mark.groups = NULL,
main = "Directed and Weighted"
)
graph_rhesus_dir_unw <- graph_from_data_frame(data_rhesus[, 1:2])
com_dir_unw <- cluster_walktrap(graph_rhesus_dir_unw)
plot(
com_dir_unw,
graph_rhesus_dir_unw,
vertex.size = 8,
vertex.label.cex = 0.6,
vertex.label.color = "white",
edge.curved = 0,
edge.arrow.size = 0.5,
edge.arrow.width = 0.5,
# mark.groups = NULL,
main = "Directed but Unweighted"
)
graph_rhesus_und_wei <- graph_from_data_frame(data_rhesus, directed = FALSE)
com_und_wei <- cluster_walktrap(graph_rhesus_und_wei)
plot(
com_und_wei,
graph_rhesus_und_wei,
vertex.size = 8,
vertex.label.cex = 0.6,
vertex.label.color = "white",
edge.curved = 0,
edge.label = E(graph_rhesus_und_wei)$weight,
edge.label.cex = 0.6,
edge.label.color = "black",
# mark.groups = NULL,
main = "Undirected but Weighted"
)
graph_rhesus_und_unw <- graph_from_data_frame(data_rhesus[, 1:2], directed = FALSE)
com_und_unw <- cluster_walktrap(graph_rhesus_und_unw)
plot(
com_und_unw,
graph_rhesus_und_unw,
vertex.size = 8,
vertex.label.cex = 0.6,
vertex.label.color = "white",
edge.curved = 0,
# mark.groups = NULL,
main = "Undirected and Unweighted"
)
```