如何在R中向量化（并加快）此递归图搜索功能？

Question

I wrote a recursive function in R for finding all paths st paths of a directed graph (no cycles). I used this page as my model: All possible paths from one node to another in a directed tree (igraph) and it outputs the correct result, but it's slow. With small graphs, no big deal. With large graphs, it's an issue.

I'm new to R but have read that it performs significantly better when avoiding loops and using vectorization. I'm trying to wrap my head around it, and am hoping you might assist. My code:

findAllPaths <- function(graph,start,end) {
  return(fastFindPaths(graph, start, end))
}   


fastFindPaths <- function(graph, from, to, path) {
  if(missing(path)) path <- c()
  path <- cbind(path, from)
  if (from == to) return(path)
  paths <- c()
  adjList <- get.adjlist(graph, mode="out")[[from]]
  for (child in adjList) {
    if (!child %in% path) {
      childPaths <- fastFindPaths(graph, child, to, path)
      for (childPath in childPaths) paths <- c(paths, childPath)
    }
  }
  return(paths)
}

So, is this a candidate for vectorization? How can I speed this up? Any other tips you'd give someone learning R?

Thanks!

Answer 1

igraph的开发版本具有get.all.simple.paths()函数，您可以从此处获取： http : get.all.simple.paths() 。

Answer 2

I used Tamas's suggestion to not call get.adjlist() but instead use neighbors() and that provided a decent speed increase. What really helped improve the performance, however, was parallelizing the search, by calling fastFindPaths() on each of the start node's children and aggregating the results. I'm currently on a Windows machine, so used the clusterApply() function. Though I will likely generalize the code to check the Sys.info()[1] and use mclapply() if not Windows.