Hello, I am new to igraph (and R for that matter) and have come across a problem I've been unable solve. I am trying to implement the algorithm in this <https://arxiv.org/abs/q-bio/0611020> paper (see bottom of pg. 3). In brief, the algorithm is a way to explore the assortativity-clustering space of a network. This is done by using degree distribution-preserving rewiring to alter assortativity and clustering levels in a given network. The idea is then to measure some outcomes (e.g., robustness) at different points or "pixels" in the assortativity-clustering space and see how the observed network compares to outcomes associated with different assortativity and clustering levels.
The procedure is computationally intensive so I am running it in parallel on a Linux-based high-powered computer running R version 3.4.1 and igraph version 1.1.0 (system specs here <https://www.american.edu/cas/hpc/software.cfm>). Note that we had some difficulties installing igraph on the machine and the system administrator resorted to installing a dev version. When running my code I run into two types of errors. The first is "Error: cons memory exhausted (limit reached?)." The memory available should be sufficient for this job, so I am not sure what is going on here. The second error is a bit more concerning: *** caught segfault *** address 0x1, cause 'memory not mapped' Traceback: 1: .Call("R_igraph_finalizer", PACKAGE = "igraph") 2: with$fun(list(119, FALSE, c(110, 73, 80, 21, 94, 104, 102, 57, 99, 73, 57, 60, 59, 55, 82, 40, 38, 107, 111, 57, 80, 108, 53, 93, 100, 104, 107, 114, 73, 104, 85, 111, [TEXT REMOVED TO SAVE SPACE] "100", "101", "102", "103", "104", "105", "106", "107", "108", "109", "110", "111", "112", "113", "114", "115", "116", "117", "118", "119")), list()), <environment>), loops = FALSE, niter = 1) 3: eval(call, .env) 4: eval(call, .env) 5: do_call(with$fun, list(graph), .args = with$args) 6: rewire(ntw1, with = keeping_degseq(loops = FALSE, niter = 1)) 7: sampling(network, pixels, min.r, max.r, min.c, max.c, vrnd) 8: FUN(X[[i]], ...) 9: lapply(X = S, FUN = FUN, ...) 10: doTryCatch(return(expr), name, parentenv, handler) 11: tryCatchOne(expr, names, parentenv, handlers[[1L]]) 12: tryCatchList(expr, classes, parentenv, handlers) 13: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call)[1L] prefix <- paste("Error in", dcall, ": ") LONG <- 75L msg <- conditionMessage(e) sm <- strsplit(msg, "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && identical(getOption("show.error.messages"), TRUE)) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))}) 14: try(lapply(X = S, FUN = FUN, ...), silent = TRUE) 15: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE)) 16: FUN(X[[i]], ...) 17: lapply(seq_len(cores), inner.do) 18: mclapply(1:vsamp, function(samp) sampling(network, pixels, min.r, max.r, min.c, max.c, vrnd), mc.cores = no.cores) An irrecoverable exception occurred. R is aborting now ... Any ideas why this would arise? My code will run just fine for a number of iterations, it is just that a few iterations will fail because of these errors. I have tried to develop a simplified implementation of the algorithm and that code is available here <https://www.heath-henderson.com/research.html>. (See the code for "The structure of risk-sharing networks" under Works in Progress. The data is provided in that same directory.) The key function for the procedure is called "sampling" and I have pasted it here in case somebody can spot obvious issues: sampling <- function(network, pixels, min.r, max.r, min.c, max.c, stop){ pixel.count <- nrow(pixels) pixels <- pixels[sample(1:pixel.count),] # Randomize pixel order pixels <- as.list(pixels) # Pixels to list results <- vector("list", pixel.count) # List to store results ntw1 <- network r1 <- assortativity_degree(ntw1) # Calculate initial assortativity coeff. c1 <- transitivity(ntw1, type="global", isolates="zero") # Calculate initial clustering coeff. for (octr in 1:pixel.count){ # Run for each pixel lb.r <- pixels[[1]][octr] # Unpack lower and upper bounds for pixel ub.r <- pixels[[2]][octr] lb.c <- pixels[[3]][octr] ub.c <- pixels[[4]][octr] center.r <- (lb.r + ub.r)/2 # Calculate pixel center center.c <- (lb.c + ub.c)/2 rd1 <- ((r1 - center.r)/(max.r - min.r))^2 # Calculate components of distance formula cd1 <- ((c1 - center.c)/(max.c - min.c))^2 d1 <- sqrt(rd1 + cd1) # Calculate initital distance cond1 <- (r1 < lb.r) || (r1 >= ub.r) cond2 <- (c1 < lb.c) || (c1 >= ub.c) ictr <- 1 # Initialize inner counter while ((cond1 || cond2) && ictr <= 1e+05){ # While outside pixel ... ntw2 <- rewire(ntw1, with=keeping_degseq(loops=FALSE, niter=1)) # One trial r2 <- assortativity_degree(ntw2) # Calculate new assortativity coeff. c2 <- transitivity(ntw2, type="global", isolates="zero") # Calculate new clustering coeff. rd2 <- ((r2 - center.r)/(max.r - min.r))^2 cd2 <- ((c2 - center.c)/(max.c - min.c))^2 d2 <- sqrt(rd2 + cd2) # Calculate new distance if (d2 < d1) { # If moving in the right direction ... ntw1 <- ntw2 # ... update values r1 <- r2 c1 <- c2 d1 <- d2 cond1 <- (r1 < lb.r) || (r1 >= ub.r) # Check if still outside pixel cond2 <- (c1 < lb.c) || (c1 >= ub.c) } ictr <- ictr + 1 } if (ictr <= 1e+05){ # If exit was successful ... ntw3 <- walk(ntw1, lb.r, ub.r, lb.c, ub.c, stop) # Execute walk dist <- mean_distance(ntw3) # Calculate stats component <- max(components(ntw3)$csize) random.50 <- f.random(ntw3, 0.50, 1) targeted.50 <- f.targeted(ntw3, 0.50) targeted.75 <- f.targeted(ntw3, 0.75) results[[octr]] <- list(dist, component, random.50, targeted.50, targeted.75) } else {results[[octr]] <- rep(list(NA), 5)} } } The inputs for the function include the network, a data table with the assortativity-clustering coordinates to visit ("pixels"), the overall maximum and minimum values of the assortativity-clustering space, and a stopping criterion. Any help would be much appreciated, please let me know if any additional information is necessary. Thanks! Heath -- *Heath Henderson, PhD* Assistant Professor of Economics Drake University College of Business and Public Administration 359 Aliber Hall, Des Moines, IA 50311 515.271.2898 (Office) 612.867.4776 (Cell) heath-henderson.com
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