The package includes all centrality indices implemented in igraph and additionally all that are made available in the netrankr package. All indices can be found in the function group centrality_*().
flo_tidy %>%
activate("nodes") %>%
mutate(
degree = centrality_degree(),
betweenness = centrality_betweenness()
) %>%
ggraph("stress", bbox = 10) +
geom_edge_link0(edge_color = "black") +
geom_node_point(shape = 21, aes(size = degree, fill = betweenness)) +
geom_node_text(aes(label = name)) +
scale_fill_gradient(low = "#104E8B", high = "#CD2626") +
scale_size(range = c(4, 10)) +
theme_graph()
Similar to centrality, all clustering algorithms from igraph are available via group_*()
# create random graph with group structure (igraph equivalent is sample_islands())
play_islands(4, 12, 0.8, 4) %>%
mutate(community = as.factor(group_louvain())) %>%
ggraph(layout = "stress") +
geom_edge_link0() +
geom_node_point(aes(fill = community), shape = 21, size = 6) +
theme_graph()
Coupling this with what we learned above, we can color the edges according to the cluster they belong to.
play_islands(4, 12, 0.8, 4) %>%
mutate(community = as.factor(group_louvain())) %>%
activate("edges") %>%
mutate(community = as.factor(ifelse(.N()$community[from] == .N()$community[to], .N()$community[from], 5))) %>%
ggraph(layout = "stress") +
geom_edge_link0(aes(edge_colour = community), show.legend = FALSE) +
geom_node_point(aes(fill = community), shape = 21, size = 6) +
scale_fill_brewer(palette = "Set3") +
scale_edge_color_brewer(palette = "Set3") +
theme_graph(background = "grey88")
tidygraphs harmonizes many other available functions in igraph to make them easier accessible. The best way to check what is available is to look at the function groups node_*() and edge_*(). Some simple examples are shown below.
# the node id of the Medici is 9
flo_tidy %>%
activate("nodes") %>%
mutate(dist2Medici = node_distance_to(nodes = 9)) %>%
activate("edges") %>%
mutate(edge2Medici = edge_is_incident(9)) %>%
ggraph("stress") +
geom_edge_link0(aes(edge_color = edge2Medici)) +
geom_node_point(aes(fill = dist2Medici), size = 9, shape = 21) +
theme_graph()