Merge pull request #37 from maelle/igraph

chore: update to igraph's new function names

Author: mbojan

R/proxfun.R

@@ -129,7 +129,7 @@ proxfun.igraph <- function(graph, method, v1 = NULL, v2 = v1,
                            value =c("matrix", "edgelist", "graph"), ...){
   # temporary issue - break code if graph is directed or disconnected until
   # proper functions are implemented
-  if (igraph::is.directed(graph) | !igraph::is.connected(graph))
+  if (igraph::is_directed(graph) | !igraph::is_connected(graph))
     stop("graph has to be undirected and connected")
 
   value <- match.arg(value)

R/proxfun_utillity.R

@@ -21,7 +21,7 @@ check_vertices <- function(graph, v){
       stop("Vertex id out of range")
     v
   } else if (is.character(v)){
-    if (!igraph::is.named(graph))
+    if (!igraph::is_named(graph))
       stop("Graph does not have vertex names. Provide vertex IDs")
     v <- match(v, igraph::V(graph)$name)
     if (any(is.na(v)))
@@ -61,10 +61,10 @@ coerce_result <- function(result, value, graph){
     result <- result[order(as.numeric(rownames(result))),
                      order(as.numeric(colnames(result)))]
     # create weighted graph
-    g <- igraph::graph.adjacency(result, weighted = TRUE, add.colnames = NA,
-                         mode = ifelse(igraph::is.directed(graph), "directed", "undirected"))
+    g <- igraph::graph_from_adjacency_matrix(result, weighted = TRUE, add.colnames = NA,
+                         mode = ifelse(igraph::is_directed(graph), "directed", "undirected"))
     # empty original graph and add weighted graph to preserve structure
-    g <- igraph::graph.union(igraph::delete.edges(graph, igraph::E(graph)), g,
+    g <- igraph::union(igraph::delete_edges(graph, igraph::E(graph)), g,
                      byname = FALSE)
     return(g)
   }

R/similarity_act.R

@@ -8,7 +8,7 @@
 #
 
 similarity_act <- function(graph, v1, v2, ...){
-  L <- igraph::graph.laplacian(graph)
+  L <- igraph::laplacian_matrix(graph)
   n <- igraph::vcount(graph)
   m <- igraph::ecount(graph)
 

R/similarity_act_n.R

@@ -8,7 +8,7 @@
 #
 
 similarity_act_n <- function(graph, v1, v2, ...){
-  L <- igraph::graph.laplacian(graph)
+  L <- igraph::laplacian_matrix(graph)
   n <- igraph::vcount(graph)
   m <- igraph::ecount(graph)
   deg <- igraph::degree(graph)

R/similarity_cos_l.R

@@ -7,7 +7,7 @@
 #
 
 similarity_cos_l <- function(graph, v1, v2, ...){
-  L <- igraph::graph.laplacian(graph)
+  L <- igraph::laplacian_matrix(graph)
   n <- igraph::vcount(graph)
   m <- igraph::ecount(graph)
 

R/similarity_dist.R

@@ -11,7 +11,7 @@
 #
 
 similarity_dist <- function(graph, v1, v2, ...){
-  score <- igraph::shortest.paths(graph, v = v1, to = v2)
+  score <- igraph::distances(graph, v = v1, to = v2)
   score[is.infinite(score)] <- igraph::vcount(graph) + 1
   # maybe infinity here ?
   score <- 1 / score

R/similarity_jaccard.R

@@ -3,11 +3,12 @@
 # Similarity measure counting the proportion of shared nodes.
 #
 # This is a simple wrapper to an \pkg{igraph} function
-#   \code{\link[igraph]{similarity.jaccard}}, which calculates the proportion
+#   \code{\link[igraph]{similarity}} with \code{method="jaccard"}, 
+#   which calculates the proportion
 #   of shared neighbors of two vertices.
 #
 
 similarity_jaccard <- function(graph, v1, v2, ...){
-  score <- igraph::similarity.jaccard(graph)
+  score <- igraph::similarity(graph, method = "jaccard")
   score[v1, v2]
 }

R/similarity_katz.R

@@ -7,7 +7,7 @@
 #
 
 similarity_katz <- function(graph, v1, v2, beta = 0.001, ...){
-  A  <- igraph::get.adjacency(graph)
+  A  <- igraph::as_adjacency_matrix(graph)
   #I <- Diagonal(nrow(A))
   I <- diag(nrow(A))
   tmp <- I - beta * A

R/similarity_l.R

@@ -8,7 +8,7 @@
 #
 
 similarity_l <- function(graph, v1, v2, ...){
-  L <- igraph::graph.laplacian(graph)
+  L <- igraph::laplacian_matrix(graph)
   n <- igraph::vcount(graph)
 
   score <- solve(L - 1/n) + 1/n

R/similarity_lhn_global.R

@@ -8,9 +8,9 @@
 #
 
 similarity_lhn_global <- function(graph, v1, v2, theta = 0.5, ...){
-  lambda <- igraph::graph.eigen(graph)$value
+  lambda <- igraph::spectrum(graph)$value
   deg <- igraph::degree(graph)
-  A <- igraph::get.adjacency(graph)
+  A <- igraph::as_adjacency_matrix(graph)
   #I <- Diagonal(nrow(adj))
   I <- diag(nrow(A))
   tmp <- I - (theta / lambda) * A