Hey Andreas, let's tackle this edge weight update bottleneck you're facing with large igraph objects. I've gone through your code and the approaches you've tried, so here are some targeted optimizations that should help speed things up:

First, let's clear up the confusion around parallel processing: even though igraph is thread-safe, R's parallel frameworks like mclapply create copies of your graph object in each child process. Any changes you make to the graph inside a parallel loop only modify the local copy, not the original graph in your main session. That's why you have to collect results in the main process and assign them back—this isn't an igraph limitation, it's how R handles parallel memory.

The biggest speedup will come from ditching repeated igraph index lookups and using vectorized operations instead. Your current code accesses E(gr)[selected.edges]$weight twice, which triggers two separate attribute queries. Instead, work directly with the full weight vector:

# Grab the entire weight vector once
weights <- E(gr)$weight

# Update only the relevant edges in the vector
weights[selected.edges] <- weights[selected.edges] * 10

# Assign the modified vector back to the graph in one step
E(gr)$weight <- weights

This cuts down on igraph's internal overhead drastically, especially with large graphs. Also, add selected.edges <- unique(selected.edges) if there are duplicate edge IDs—no need to update the same edge multiple times.

If you still need parallel processing (e.g., for more complex weight calculations), minimize the work each child process does. Focus only on the math, not touching the graph object until the main process:

library(parallel)
no_cores <- detectCores(logical = FALSE)

# Get the full weight vector first
weights <- E(gr)$weight

# Split edge IDs into chunks for each core
edge_chunks <- split(selected.edges, factor(sort(rank(selected.edges) %% no_cores)))

# Parallel compute the updated weights (only math, no graph operations)
updated_chunks <- mclapply(edge_chunks, function(chunk) {
  weights[chunk] * 10  # Replace with your actual calculation here
}, mc.cores = no_cores)

# Merge results back into the original weight vector
for (i in seq_along(edge_chunks)) {
  weights[edge_chunks[[i]]] <- updated_chunks[[i]]
}

# Assign back to the graph
E(gr)$weight <- weights

This avoids copying the entire graph to each child process and reduces inter-process communication overhead.

If you're running this edge-finding logic in a loop, precompute a lookup table for node-to-edge relationships to avoid repeated calls to incident_edges:

# Run once before your loop
node_to_edges <- lapply(V(gr), function(v) incident_edges(gr, v))

# Then in your loop, get selected edges in O(n) time
selected.edges <- unlist(node_to_edges[wished.nodes$ids_igraph])
selected.edges <- unique(selected.edges)

This eliminates the need for igraph to traverse the entire graph every time you need edges for specific nodes.

For maximum speed (especially with huge graphs), use Rcpp to interact directly with igraph's C backend, bypassing R's vector copy overhead. Here's a simple example:

#include <Rcpp.h>
#include <igraph.h>

// [[Rcpp::export]]
void update_edge_weights(SEXP graph, Rcpp::IntegerVector edge_ids, double multiplier) {
  igraph_t* g = reinterpret_cast<igraph_t*>(R_ExternalPtrAddr(graph));
  igraph_vector_t weights;
  
  // Retrieve the weight attribute
  igraph_vector_init(&weights, 0);
  igraph_cattribute_VAN_get(g, "weight", &weights);
  
  // Update edges (note: igraph uses 0-based indices, R uses 1-based)
  for (int i = 0; i < edge_ids.size(); ++i) {
    int eid = edge_ids[i] - 1;
    VECTOR(weights)[eid] *= multiplier;
  }
  
  // Save the updated weights back to the graph
  igraph_cattribute_VAN_set(g, "weight", &weights);
  
  igraph_vector_destroy(&weights);
}

Call it in R like this:

update_edge_weights(gr, selected.edges, 10)

This will outperform pure R code by a wide margin for large edge sets.

• Always profile your code with profvis or system.time() to confirm where the slowdown is—sometimes the bottleneck is in the distance calculation (your crossing step) rather than the weight update.
• For simple multiplications, vectorized operations (option 2) will often be faster than parallel processing, since parallel has inherent overhead from spawning processes.

内容的提问来源于stack exchange，提问作者Andreas