如何创建igraph多图列表

我有许多网络，目前表示为edgelists，我可以从中创建单个igraph对象：

> nodes <- read.csv("nodes.csv",header=T,as.is=T)
> links <- read.csv("edges.csv",header=T,as.is=T)
> net <- graph_from_data_frame(d=links, vertices=nodes, directed=T)
> net
IGRAPH f40255d DNW- 255 458 -- 
+ attr: name (v/c), word (v/c), type (e/c), weight (e/n)
+ edges from f40255d (vertex names):
 [1] s001->s002 s001->s004 s001->s006 s001->s013 s001->s018 s001->s020
 [7] s001->s025 s001->s027 s001->s031 s001->s032 s001->s033 s001->s034
[13] s001->s035 s001->s037 s001->s038 s001->s041 s001->s042 s001->s044
[19] s001->s046 s001->s047 s001->s050 s001->s051 s001->s052 s001->s055
[25] s001->s059 s001->s060 s001->s064 s001->s065 s001->s067 s001->s068
[31] s001->s069 s001->s070 s001->s072 s001->s074 s001->s075 s001->s078
[37] s001->s081 s001->s088 s001->s091 s001->s092 s001->s093 s001->s098
[43] s001->s100 s001->s103 s001->s111 s001->s112 s001->s112 s001->s118
+ ... omitted several edges

>节点链接网络
IGRAPHF40255D DNW-255 458--
+属性：名称（v/c）、单词（v/c）、类型（e/c）、重量（e/n）
+f40255d中的边（顶点名称）：
[1] s001->s002 s001->s004 s001->s006 s001->s013 s001->s018 s001->s020
[7] s001->s025 s001->s027 s001->s031 s001->s032 s001->s033 s001->s034
[13] s001->s035 s001->s037 s001->s038 s001->s041 s001->s042 s001->s044
[19] s001->s046 s001->s047 s001->s050 s001->s051 s001->s052 s001->s055
[25]s001->s059 s001->s060 s001->s064 s001->s065 s001->s067 s001->s068
[31]s001->s069 s001->s070 s001->s072 s001->s074 s001->s075 s001->s078
[37]s001->s081 s001->s088 s001->s091 s001->s092 s001->s093 s001->s098
[43]s001->s100 s001->s103 s001->s111 s001->s112 s001->s112 s001->s118
+ ... 省略了几个边

但是我想分析我想使用R中的graphkernels包分析这些，它需要一个igraph图列表。我已经看过这些文档了，但是我找不到任何关于如何将其中的一些文档变成一个列表的指导。谁能给我指出正确的方向吗

对不起，如果这是一个愚蠢的问题。我至少精通R

更新：我尝试创建3个igraph对象，并使用

g <- list(net1,net2,net3)

---

g结果这就是你应该如何制作一个图表列表。我认为这个问题可能在你的图表中。以下内容对我很有用：
g1谢谢。知道如何调试图表吗？错误消息的信息量不大。警告说明了什么？我将从一系列较小的图表开始。此外，我还将检查所有图形是否属于类似的类型：即所有图形都是命名的或未命名的，都具有相同的顶点和边属性，并且所有这些属性是否属于类似的类型（字符、数字、布尔值）
> result <- CalculateWLKernel(g,5)
Error in CalculateKernelCpp(graph.info.list, par, 11) : 
  vector::_M_default_append
In addition: There were 12 warnings (use warnings() to see them)