使用 tidygraph 为每个节点计算给定类型的边

Count edges of a given type for each node with tidygraph

Objective:我想统计每个节点的部分类型的入边数。我该怎么做?

条件:

以下代码将生成一个示例图

g <- play_erdos_renyi(n = 20, p = .10) %>% 
  activate(edges) %>% 
  mutate(type = sample(c('a', 'b', 'c'), size = n(), replace = T))

理想的输出,当搜索(例如)传入的 g 边时,如果类型“a”看起来像:

Node   type_a_edges
X           3
Y           1
Z           4
...

编辑:添加了一张图使问题更具体。

这将产生与请求一致的输出

g %>%  
  activate(nodes) %>% 
  mutate(
    indegree_type_a = centrality_degree(
      weights = as.numeric(.E()$type == 'a'),
      mode = 'in'))

这是一个 tidygraph + dplyr 选项

g %>%
  activate(edges) %>%
  filter(type == "a") %>%
  as_tibble() %>%
  group_by(to) %>%
  summarise(indegree_a = n())

它给出所有类型“A”的向内边缘的计数

# A tibble: 8 x 2
     to indegree_a
  <int>      <int>
1     3          1
2     5          1
3     8          2
4    11          1
5    12          1
6    15          2
7    17          2
8    18          2

如果想获得所有节点的完整信息,可以试试下面的代码

g %>%
  activate(edges) %>%
  as_tibble() %>%
  select(-from) %>%
  mutate(counts = 1) %>%
  arrange(type) %>%
  pivot_wider(
    names_from = type,
    values_from = counts,
    values_fill = 0, values_fn = sum, names_glue = "indegree_{.name}"
  ) %>%
  arrange(to)

这给出了

# A tibble: 18 x 4
      to indegree_a indegree_b indegree_c
   <int>      <dbl>      <dbl>      <dbl>
 1     1          0          1          2
 2     2          0          0          1
 3     3          1          0          0
 4     4          0          2          0
 5     5          1          0          2
 6     6          0          1          1
 7     8          2          1          0
 8     9          0          0          1
 9    10          0          0          1
10    11          1          1          0
11    12          1          0          1
12    13          0          1          2
13    15          2          0          0
14    16          0          3          0
15    17          2          0          0
16    18          2          1          0
17    19          0          0          1
18    20          0          1          0