Merge branch 'main' into fix/ncol-lgl

.github/workflows/build-and-check.yml

@@ -113,9 +113,9 @@ jobs:
       matrix:
         config:
           - {os: windows-latest, r: 'release'}
-          - {os: macOS-latest,   r: 'release'}
+          # - {os: macOS-latest,   r: 'release'}
           - {os: ubuntu-20.04,   r: 'release',  rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
-          - {os: ubuntu-20.04,   r: 'devel',    rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          # - {os: ubuntu-20.04,   r: 'devel',    rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
           - {os: ubuntu-20.04,   r: 'oldrel-1', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
           - {os: ubuntu-20.04,   r: 'oldrel-2', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
           - {os: ubuntu-20.04,   r: 'oldrel-3', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: igraph
-Version: 2.0.3.9012
+Version: 2.0.3.9018
 Title: Network Analysis and Visualization
 Authors@R: c(
     person("Gábor", "Csárdi", , "csardi.gabor@gmail.com", role = "aut",

NEWS.md

@@ -1,5 +1,55 @@
 <!-- NEWS.md is maintained by https://fledge.cynkra.com, contributors should not edit this file -->
 
+# igraph 2.0.3.9018
+
+## Bug fixes
+
+- Fixed potential memory leak in `R_igraph_community_to_membership2()` (#1367).
+
+
+# igraph 2.0.3.9017
+
+## Bug fixes
+
+- Comment failing ci (#1357).
+
+## Testing
+
+- Refactoring around `interface.R` (#1366).
+
+## Uncategorized
+
+- Fix `%\>%` typo in vignettes (#1343).
+
+
+# igraph 2.0.3.9016
+
+## doc
+
+- Improve `eigen_centrality()` documentation.
+
+
+# igraph 2.0.3.9015
+
+## doc
+
+- Further improved `cluster_edge_betweenness()` documentation.
+
+
+# igraph 2.0.3.9014
+
+## doc
+
+- Improved `cluster_edge_betweenness()` documentation.
+
+
+# igraph 2.0.3.9013
+
+## Testing
+
+- Improve sample_degseq() tests (#1315).
+
+
 # igraph 2.0.3.9012
 
 ## Continuous integration

R/centrality.R

@@ -892,40 +892,40 @@ eigen_defaults <- function() {
   )
 }
 
-#' Find Eigenvector Centrality Scores of Network Positions
+#' Eigenvector centrality of vertices
 #'
 #' `eigen_centrality()` takes a graph (`graph`) and returns the
-#' eigenvector centralities of positions `v` within it
+#' eigenvector centralities of the vertices `v` within it.
 #'
-#' Eigenvector centrality scores correspond to the values of the first
-#' eigenvector of the graph adjacency matrix; these scores may, in turn, be
+#' Eigenvector centrality scores correspond to the values of the principal
+#' eigenvector of the graph's adjacency matrix; these scores may, in turn, be
 #' interpreted as arising from a reciprocal process in which the centrality of
 #' each actor is proportional to the sum of the centralities of those actors to
 #' whom he or she is connected.  In general, vertices with high eigenvector
 #' centralities are those which are connected to many other vertices which are,
-#' in turn, connected to many others (and so on).  (The perceptive may realize
+#' in turn, connected to many others (and so on).  The perceptive may realize
 #' that this implies that the largest values will be obtained by individuals in
 #' large cliques (or high-density substructures).  This is also intelligible
 #' from an algebraic point of view, with the first eigenvector being closely
 #' related to the best rank-1 approximation of the adjacency matrix (a
 #' relationship which is easy to see in the special case of a diagonalizable
 #' symmetric real matrix via the \eqn{SLS^-1}{$S \Lambda S^{-1}$}
-#' decomposition).)
+#' decomposition).
 #'
 #' The adjacency matrix used in the eigenvector centrality calculation assumes
-#' that loop edges are counted *twice*; this is because each loop edge has
-#' *two* endpoints that are both connected to the same vertex, and you
-#' could traverse the loop edge via either endpoint.
+#' that loop edges are counted *twice* in undirected graphs; this is because
+#' each loop edge has *two* endpoints that are both connected to the same vertex,
+#' and you could traverse the loop edge via either endpoint.
 #'
 #' In the directed case, the left eigenvector of the adjacency matrix is
 #' calculated. In other words, the centrality of a vertex is proportional to
 #' the sum of centralities of vertices pointing to it.
 #'
-#' Eigenvector centrality is meaningful only for connected graphs. Graphs that
-#' are not connected should be decomposed into connected components, and the
-#' eigenvector centrality calculated for each separately. This function does
-#' not verify that the graph is connected. If it is not, in the undirected case
-#' the scores of all but one component will be zeros.
+#' Eigenvector centrality is meaningful only for (strongly) connected graphs.
+#' Undirected graphs that are not connected should be decomposed into connected
+#' components, and the eigenvector centrality calculated for each separately.
+#' This function does not verify that the graph is connected. If it is not, in
+#' the undirected case the scores of all but one component will be zeros.
 #'
 #' Also note that the adjacency matrix of a directed acyclic graph or the
 #' adjacency matrix of an empty graph does not possess positive eigenvalues,

R/community.R

@@ -1565,26 +1565,24 @@ cluster_walktrap <- function(graph, weights = NULL, steps = 4,
 
 #' Community structure detection based on edge betweenness
 #'
-#' Many networks consist of modules which are densely connected themselves but
-#' sparsely connected to other modules.
-#'
-#' The edge betweenness score of an edge measures the number of shortest paths
-#' through it, see [edge_betweenness()] for details. The idea of the
-#' edge betweenness based community structure detection is that it is likely
-#' that edges connecting separate modules have high edge betweenness as all the
-#' shortest paths from one module to another must traverse through them. So if
-#' we gradually remove the edge with the highest edge betweenness score we will
-#' get a hierarchical map, a rooted tree, called a dendrogram of the graph. The
-#' leafs of the tree are the individual vertices and the root of the tree
-#' represents the whole graph.
-#'
-#' `cluster_edge_betweenness()` performs this algorithm by calculating the
-#' edge betweenness of the graph, removing the edge with the highest edge
-#' betweenness score, then recalculating edge betweenness of the edges and
-#' again removing the one with the highest score, etc.
-#'
-#' `edge.betweeness.community` returns various information collected
-#' through the run of the algorithm. See the return value down here.
+#' Community structure detection based on the betweenness of the edges
+#' in the network. This method is also known as the Girvan-Newman
+#' algorithm.
+#'
+#' The idea behind this method is that the betweenness of the edges connecting
+#' two communities is typically high, as many of the shortest paths between
+#' vertices in separate communities pass through them. The algorithm
+#' successively removes edges with the highest betweenness, recalculating
+#' betweenness values after each removal. This way eventually the network splits
+#' into two components, then one of these components splits again, and so on,
+#' until all edges are removed. The resulting hierarhical partitioning of the
+#' vertices can be encoded as a dendrogram.
+#'
+#' `cluster_edge_betweenness()` returns various information collected
+#' through the run of the algorithm. Specifically, `removed.edges` contains
+#' the edge IDs in order of the edges' removal; `edge.betweenness` contains
+#' the betweenness of each of these at the time of their removal; and
+#' `bridges` contains the IDs of edges whose removal caused a split.
 #'
 #' @param graph The graph to analyze.
 #' @param weights The weights of the edges. It must be a positive numeric vector,

src/rinterface_extra.c

@@ -4967,9 +4967,10 @@ SEXP R_igraph_degree_sequence_game(SEXP pout_seq, SEXP pin_seq,
   igraph_integer_t method=(igraph_integer_t) REAL(pmethod)[0];
   SEXP result;
 
-  R_SEXP_to_vector_int_copy(pout_seq, &outseq);
+  IGRAPH_R_CHECK(R_SEXP_to_vector_int_copy(pout_seq, &outseq));
+  IGRAPH_FINALLY(igraph_vector_int_destroy, &outseq);
   if (!Rf_isNull(pin_seq)) {
-    R_SEXP_to_vector_int_copy(pin_seq, &inseq);
+    IGRAPH_R_CHECK(R_SEXP_to_vector_int_copy(pin_seq, &inseq));
   } else {
     IGRAPH_R_CHECK(igraph_vector_int_init(&inseq, 0));
   }
@@ -4978,7 +4979,7 @@ SEXP R_igraph_degree_sequence_game(SEXP pout_seq, SEXP pin_seq,
   PROTECT(result=R_igraph_to_SEXP(&g));
   igraph_vector_int_destroy(&outseq);
   igraph_vector_int_destroy(&inseq);
-  IGRAPH_FINALLY_CLEAN(1);
+  IGRAPH_FINALLY_CLEAN(2);
   IGRAPH_I_DESTROY(&g);
 
   UNPROTECT(1);
@@ -7341,16 +7342,18 @@ SEXP R_igraph_community_to_membership2(SEXP pmerges, SEXP pvcount,
   igraph_vector_int_t membership;
   SEXP result;
 
-  R_SEXP_to_matrix_int(pmerges, &merges);
-  igraph_vector_int_init(&membership, 0);
+  IGRAPH_R_CHECK(R_SEXP_to_matrix_int(pmerges, &merges));
+  IGRAPH_FINALLY(igraph_matrix_int_destroy, &merges);
+
+  IGRAPH_R_CHECK(igraph_vector_int_init(&membership, 0));
   IGRAPH_FINALLY(igraph_vector_int_destroy, &membership);
 
   IGRAPH_R_CHECK(igraph_community_to_membership(&merges, vcount, steps, &membership, 0));
   PROTECT(result=R_igraph_vector_int_to_SEXP(&membership));
 
   igraph_matrix_int_destroy(&merges);
   igraph_vector_int_destroy(&membership);
-  IGRAPH_FINALLY_CLEAN(1);
+  IGRAPH_FINALLY_CLEAN(2);
 
   UNPROTECT(1);
   return result;

tests/testthat/_snaps/games.md

@@ -0,0 +1,16 @@
+# sample_degseq() works -- exponential degree error
+
+    Code
+      sample_degseq(exponential_degrees, method = "vl")
+    Condition
+      Error in `sample_degseq()`:
+      ! At vendor/cigraph/src/games/degree_sequence_vl/gengraph_mr-connected.cpp:<linenumber> : Cannot make a connected graph from the given degree sequence. Invalid value
+
+# sample_degseq() works -- Power-law degree error
+
+    Code
+      sample_degseq(powerlaw_degrees, method = "vl")
+    Condition
+      Error in `sample_degseq()`:
+      ! At vendor/cigraph/src/games/degree_sequence_vl/gengraph_mr-connected.cpp:<linenumber> : Cannot realize the given degree sequence as an undirected, simple graph. Invalid value
+

tests/testthat/helper.R

@@ -25,3 +25,7 @@ expect_that <- function(object, condition, info = NULL, label = NULL) {
     condition(object)
   )
 }
+
+expect_isomorphic <- function(g1, g2) {
+  expect_true(graph.isomorphic(g1, g2))
+}